These methods were evaluated by spatial k -fold cross-validation estimation of the predictive performance , assessment of variable importance for gaining insights into model behavior and by the appearance of the prediction ( i.e. susceptibility ) map . The modeling techniques applied were logistic regression ( GLM ) , generalized additive models ( GAM ) , weights of evidence ( WOE ) , the support vector machine ( SVM ) , random forest classification ( RF ) , and bootstrap aggregated classification trees ( bundling ) with penalized discriminant analysis ( BPLDA ) . These modeling methods were tested for three areas in the province of Lower Austria , Austria . Traditional approaches for analyzing this type of data generally aggregate data over a certain timespan , or only include measurements at a fixed time point ( e.g. , formant measurements at the midpoint of a vowel ) . This paper discusses generalized additive modeling , a non-linear regression method which does not require aggregation or the pre-selection of a fixed time point . Instead , the method is able to identify general patterns over dynamically varying data , while simultaneously accounting for subject and item-related variability . We develop a generalized additive modeling framework for taking into account the effect of predictors on the dependence structure between two variables . We consider dependence or concordance measures that are solely functions of the copula , because they contain no marginal information : rank correlation coefficients or tail-dependence coefficients represent natural choices . In this paper we consider five well known approaches for function approximation . Specifically we target polynomial approximation , general additive models ( Gam ) , local regression ( Loess ) , multivariate additive regression splines ( Mars ) and artificial neural networks (Ann).Neural networks can be viewed as models of real systems , built by tuning parameters known as weights . In training the net , the problem is to find the weights that optimize its performance ( i.e. to minimize the error over the training set ) . Current data analysis procedures , however , either fail to exploit the continuous resolution provided by trajectory inference , or fail to pinpoint the exact types of differential expression . We introduce tradeSeq , a powerful generalized additive model framework based on the negative binomial distribution that allows flexible inference of both within-lineage and between-lineage differential expression . By incorporating observation-level weights , the model additionally allows to account for zero inflation . ResultsIn this paper , by using five published RNA-Seq data sets from different biological sources and with different data preprocessing schemes , we showed that commonly used estimates of gene expression levels from RNA-Seq data , such as reads per kilobase of gene length per million reads ( RPKM ) , are biased in terms of gene length , GC content and dinucleotide frequencies . We directly examined the biases at the gene-level , and proposed a simple generalized-additive-model based approach to correct different sources of biases simultaneously . Compared to previously proposed base level correction methods , our method reduces bias in gene-level expression estimates more effectively . Furthermore , the visual-inspection method was simple and rapid to implement and yielded more accurate ( relative to assumed dates of denning onset ) and precise results compared to the quantitative estimator . Although the Generalized Additive Model based approach had the advantage of estimating denning onset objectively following a set of prescribed rules in a statistical inferential framework , we found the method required significant technical capacity to implement and did not represent an improvement over simple visual-inspection-based estimates of denning onset . Published 2016 . Abstract Generalized additive models for location scale and shape ( GAMLSS ) offer very flexible solutions to a wide range of statistical analysis problems , but can be challenging in terms of proper model specification . This complex task can be simplified using regularization techniques such as gradient boosting algorithms , but the estimates derived from such models are shrunken towards zero and it is consequently not straightforward to calculate proper confidence intervals or test statistics . In order to transfer fuzzy evaluation information of objective 's attributes into objective preference information , this paper transferred fuzzy semantics into triangular fuzzy number , and used generalized additive function , an approximate multiplication and a scalar multiplication between fuzzy numbers to compute . Then transferred discrete levels of objective 's attributes to integrated levels , and used fuzzy sorting multi-indexes to define weights of method makers . They are most commonly constructed by inferring species ' occurrence – environment relationships using statistical and machine-learning methods . The variety of methods that can be used to construct SDMs ( e.g. generalized linear/additive models , tree-based models , maximum entropy , etc . ) , and the variety of ways that such models can be implemented , permits substantial flexibility in SDM complexity . Building models with an appropriate amount of complexity for the study objectives is critical for robust inference . In this paper , we address these issues through a learning-based approach using visual inputs from a camera mounted in front of a vehicle . We propose the following : 1 ) a pixel-hierarchy feature descriptor to model the contextual information shared by lane markings with the surrounding road region ; 2 ) a robust boosting algorithm to select relevant contextual features for detecting lane markings ; and 3 ) particle filters to track the lane markings , without knowledge of vehicle speed , by assuming the lane markings to be static through the video sequence and then learning the possible road scene variations from the statistics of tracked model parameters . We investigate the effectiveness of our algorithm on challenging daylight and night-time road video sequences . We propose a new boosting framework called ArchBoost that uses diminishing gradient property directly and leads to boosting algorithms that are provably robust . Along with the ArchBoost framework , a family of nonconvex losses is proposed , which leads to the new robust boosting algorithms , named adaptive robust boosting ( ARB ) . Furthermore , we develop a new breakdown point analysis and a new influence function analysis that demonstrate gains in robustness . With the AE data set , our focus is to recognize the signals collected when the wheels were dull from the signals collected when the wheels were sharp . The AdaBoost , A-boosting and the corresponding weak learners(KNN and Proto ) were used . The results indicate that ( i ) boosting does not improve the effectiveness of k-nearest neighbor but greatly improve the effectives of the prototype classifier , ( ii ) depending upon the data , AdaBoost or A- To solve these problems , a gradient descent boosting ensemble method combined with backward interval PLS ( GD-Boosting-BiPLS ) is proposed in this paper . BiPLS is used to select the effective variables for the boosting base model , and each base model is trained sequentially by resampling . The spectral segmentation parameter of BiPLS and the iteration parameter of boosting are fused , and the weight of each base model is distributed by the gradient descent strategy . We are thus driven to investigate the problem of correcting errors after boosting has done its best . This establishes a high bar for any model stacked on the boosting base model , because it is difficult to correct the few remaining errors without also accidentally undoing correct classifications at the same time . In the following sections , we first define the particular stacking approach we will use . The weak hypothesis sequences are arranged in a boosting tree , and new weak hypotheses are added to promising nodes ( both leaves and inner nodes ) of the tree using some randomized method . Theoretical results show that the proposed algorithm asymptotically achieves the performance of the base boosting algorithm applied . Experiments are provided in ranking web documents and move ordering in chess , and the results indicate that the new strategy yields better performance when the length of the sequence is limited , and converges to similar performance as the original boosting algorithms otherwise . Originally designed for classification tasks , it combines boosting methods and the SMOTE resampling strategy . We present four variants of SMOTEBoost and provide an experimental evaluation using 30 datasets with an extensive analysis of results in order to assess the ability of SMOTEBoost methods in predicting extreme target values , and their predictive trade-off concerning baseline boosting methods . SMOTEBoost is publicly available in a software package . A bias boosting technique for a 3.2V , 1.9GHz Class AB RF amplifier designed in a 30GHz BiCMOS process is presented in this paper . In a Class AB amplifier , the average current drawn from the supply depends on the input signal level . The way to combine the global contour templates with local HOGs is by adjusting the bias of a support vector machine ( SVM ) for the local classifier . The method proposed for feature combination is referred to as biased boosting . For covering the human appearance in various poses , an expectation maximization algorithm is used which is a kind of iterative algorithm is used to construct a set of representative weighted contour templates instead of manual annotation . The Elements of Statistical Learning ” by Hastie , Tibshirani and Friedman . Boosting Boosting relates to the idea of taking a class of weak learners ( meaning , their error is close to 0.5 ) and turning them into a strong classifier ( one that gets arbitrary low error ) . Perhaps the most well-known binary classification algorithm based on boosting is the Adaboost algorithm [ 1 ] ( see Fig. 1 ) . We first unify a well-known top-down decision tree induction algorithm due to [ Kearns and Mansour , 1999 ] , and discrete AdaBoost [ Freund and Schapire , 1997 ] , as two versions of a same higher-level boosting algorithm . It may be used as the basic building block to devise simple provable boosting algorithms for complex classifiers . We provide one example : the first boosting algorithm for Oblique Decision Trees , an algorithm which turns out to be simpler , faster and significantly more accurate than previous approaches . In this paper , a novel approach for learning a vision-based hand detection system is introduced . The main contribution of this paper includes robust boosting-based framework for real-time detection of a hand in unconstrained and heterogeneous environments . The proposed system makes use of efficient representative features which allows fast computation while changing the hand appearances and background . Using adaptation techniques , we created hidden Markov models for inaudible speech and we performed automatic speech recognition . The achieved results are very promising , and prove the effectiveness of NAM microphone In this paper , we also introduce our work for recognizing normal speech using NAM microphone . The idea is to take advantage of noise robustness of NAM microphone . This stream-based data representation enables area-power efficient computing units such as the multiplier implemented as an AND gate yielding savings in power of 90 % compared with digital approaches . However , a major bottleneck for computing on streams , vision sensors , and NAM approaches is the cost of analog-to-digital ( ADC ) and digital-to-stream-to-digital converters . Our NAM-ACS architecture , simplifies the sensor and eliminates the need for the expensive conversions . Communication of the amount of uncertainty , both in the input and output for NAMs , often involves a confidence or prediction interval around a given potency estimate , derived from an understanding of the variability in the data modeled . Tuning expectations of NAM performance to an understanding of the reproducibility and variability , both of traditional approaches and NAM approaches , provides a path for the adoption of NAMs as alternatives in screening chemicals for risk . Therefore , in this paper , a framework called deep normalized convolutional neural network ( DNCNN ) is proposed for imbalanced fault classification of machinery to overcome the first weakness . Meanwhile , neuron activation maximization ( NAM ) algorithm is developed to handle the second weakness . To verify the proposed methods , three bearing datasets containing single faults and compound faults are constructed with different imbalanced degrees . The total area comprises 10 fields and 4 remaining prospects and is planned to be fully developed by the year 2001 , using wet gas pipelines to route the production to either the Anjum LTS installation or the Grijpskerk SilicaGel installation . The Rotliegend reservoirs in this part of the Netherlands are very heterogeneous and require a more detailed subsurface simulation than feasible with the standard NAM tool for gas field development ( GENREM ) . In addition , the area is close to the Waddenzee and based on extensive ecological research , NAM uses a stringent , self-imposed ecological constraint , whilst evaluating the development plans for this area . In this paper , we propose a new deep learning approach , called neural association model ( NAM ) , for probabilistic reasoning in artificial intelligence . We propose to use neural networks to model association between any two events in a domain . The Saco-Casco Bays in the southern Maine are susceptible to coastal inundation due to the influence of winter storms . The high-resolution Saco-Casco Bays storm surge modeling is setup based on Finite Volume Coastal Ocean Model ( FVCOM ) , one-way nested with the Northeast Coastal Ocean Forecast System ( NECOFS ) and driven by high-resolution NECOFS/WRF and moderate-resolution NAM WRF to understand the impact of wind speed and direction on the predicting skill of the surge . The inundation model calculated a peak surge of more than 0.5 m due to winter storm Juno , at ME8418150 on 27 January 2015 . The non-symmetry and anti-packing model ( NAM ) is a general pattern representation model that has been developed to help design some efficient image representation methods . In this paper , inspired by the idea of computing moments based on the S-Tree coding ( STC ) representation and by using the NAM and extended shading ( NAMES ) approach , we propose a fast algorithm for computing lower order moments based on the NAMES representation , which takes O(N ) time where N is the number of NAM blocks . By taking three idiomatic standard gray images ‘ Lena ’ , ‘ F16 ’ , and ‘ Peppers ’ in the field of image processing as typical test objects , and by comparing our proposed algorithm with the conventional algorithm and the popular STC representation algorithm for computing the lower order moments , the theoretical and experimental results presented in this paper show that the average execution time improvement ratios of the proposed NAMES approach over the STC approach , and also the conventional approach are 26.63 % , and 82.57 % respectively while maintaining the image quality . Is It Essential and Is It Safe ? . Copyright : © 2016 Eskin NAM . This is an open-access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use , distribution , and reproduction in any medium , provided the original author and source are credited . Generation Using NAM Expansion . A new generation method of the Tow-Thomas ( TT ) circuit based on the nodal admittance matrix ( NAM ) expansion is given . AUCs of all methods for all classes were above 0.92 . Conclusions Neural network models ( CNN & NAM ) generally had higher accuracies compared to the non-neural models ( SVM ) and have a range of accuracies that comparable to the inter-annotator agreement of three neuroradiologists . The NAM method adds ability to hold the algorithm accountable for its classification via heat map generation , thereby adding an auditing feature to this neural network .
These methods were evaluated by spatial k -fold cross-validation estimation of the predictive performance , assessment of variable importance for gaining insights into model behavior and by the appearance of the prediction ( i.e. susceptibility ) map . The modeling techniques applied were logistic regression ( GLM ) , generalized additive models ( GAM ) , weights of evidence ( WOE ) , the support vector machine ( SVM ) , random forest classification ( RF ) , and bootstrap aggregated classification trees ( bundling ) with penalized discriminant analysis ( BPLDA ) . These modeling methods were tested for three areas in the province of Lower Austria , Austria .
Traditional approaches for analyzing this type of data generally aggregate data over a certain timespan , or only include measurements at a fixed time point ( e.g. , formant measurements at the midpoint of a vowel ) . This paper discusses generalized additive modeling , a non-linear regression method which does not require aggregation or the pre-selection of a fixed time point . Instead , the method is able to identify general patterns over dynamically varying data , while simultaneously accounting for subject and item-related variability .
We develop a generalized additive modeling framework for taking into account the effect of predictors on the dependence structure between two variables . We consider dependence or concordance measures that are solely functions of the copula , because they contain no marginal information : rank correlation coefficients or tail-dependence coefficients represent natural choices .
In this paper we consider five well known approaches for function approximation . Specifically we target polynomial approximation , general additive models ( Gam ) , local regression ( Loess ) , multivariate additive regression splines ( Mars ) and artificial neural networks (Ann).Neural networks can be viewed as models of real systems , built by tuning parameters known as weights . In training the net , the problem is to find the weights that optimize its performance ( i.e. to minimize the error over the training set ) .
Current data analysis procedures , however , either fail to exploit the continuous resolution provided by trajectory inference , or fail to pinpoint the exact types of differential expression . We introduce tradeSeq , a powerful generalized additive model framework based on the negative binomial distribution that allows flexible inference of both within-lineage and between-lineage differential expression . By incorporating observation-level weights , the model additionally allows to account for zero inflation .
ResultsIn this paper , by using five published RNA-Seq data sets from different biological sources and with different data preprocessing schemes , we showed that commonly used estimates of gene expression levels from RNA-Seq data , such as reads per kilobase of gene length per million reads ( RPKM ) , are biased in terms of gene length , GC content and dinucleotide frequencies . We directly examined the biases at the gene-level , and proposed a simple generalized-additive-model based approach to correct different sources of biases simultaneously . Compared to previously proposed base level correction methods , our method reduces bias in gene-level expression estimates more effectively .
Furthermore , the visual-inspection method was simple and rapid to implement and yielded more accurate ( relative to assumed dates of denning onset ) and precise results compared to the quantitative estimator . Although the Generalized Additive Model based approach had the advantage of estimating denning onset objectively following a set of prescribed rules in a statistical inferential framework , we found the method required significant technical capacity to implement and did not represent an improvement over simple visual-inspection-based estimates of denning onset . Published 2016 .
Abstract Generalized additive models for location scale and shape ( GAMLSS ) offer very flexible solutions to a wide range of statistical analysis problems , but can be challenging in terms of proper model specification . This complex task can be simplified using regularization techniques such as gradient boosting algorithms , but the estimates derived from such models are shrunken towards zero and it is consequently not straightforward to calculate proper confidence intervals or test statistics .
In order to transfer fuzzy evaluation information of objective 's attributes into objective preference information , this paper transferred fuzzy semantics into triangular fuzzy number , and used generalized additive function , an approximate multiplication and a scalar multiplication between fuzzy numbers to compute . Then transferred discrete levels of objective 's attributes to integrated levels , and used fuzzy sorting multi-indexes to define weights of method makers .
They are most commonly constructed by inferring species ' occurrence – environment relationships using statistical and machine-learning methods . The variety of methods that can be used to construct SDMs ( e.g. generalized linear/additive models , tree-based models , maximum entropy , etc . ) , and the variety of ways that such models can be implemented , permits substantial flexibility in SDM complexity . Building models with an appropriate amount of complexity for the study objectives is critical for robust inference .
In this paper , we address these issues through a learning-based approach using visual inputs from a camera mounted in front of a vehicle . We propose the following : 1 ) a pixel-hierarchy feature descriptor to model the contextual information shared by lane markings with the surrounding road region ; 2 ) a robust boosting algorithm to select relevant contextual features for detecting lane markings ; and 3 ) particle filters to track the lane markings , without knowledge of vehicle speed , by assuming the lane markings to be static through the video sequence and then learning the possible road scene variations from the statistics of tracked model parameters . We investigate the effectiveness of our algorithm on challenging daylight and night-time road video sequences .
We propose a new boosting framework called ArchBoost that uses diminishing gradient property directly and leads to boosting algorithms that are provably robust . Along with the ArchBoost framework , a family of nonconvex losses is proposed , which leads to the new robust boosting algorithms , named adaptive robust boosting ( ARB ) . Furthermore , we develop a new breakdown point analysis and a new influence function analysis that demonstrate gains in robustness .
With the AE data set , our focus is to recognize the signals collected when the wheels were dull from the signals collected when the wheels were sharp . The AdaBoost , A-boosting and the corresponding weak learners(KNN and Proto ) were used . The results indicate that ( i ) boosting does not improve the effectiveness of k-nearest neighbor but greatly improve the effectives of the prototype classifier , ( ii ) depending upon the data , AdaBoost or A-
To solve these problems , a gradient descent boosting ensemble method combined with backward interval PLS ( GD-Boosting-BiPLS ) is proposed in this paper . BiPLS is used to select the effective variables for the boosting base model , and each base model is trained sequentially by resampling . The spectral segmentation parameter of BiPLS and the iteration parameter of boosting are fused , and the weight of each base model is distributed by the gradient descent strategy .
We are thus driven to investigate the problem of correcting errors after boosting has done its best . This establishes a high bar for any model stacked on the boosting base model , because it is difficult to correct the few remaining errors without also accidentally undoing correct classifications at the same time . In the following sections , we first define the particular stacking approach we will use .
The weak hypothesis sequences are arranged in a boosting tree , and new weak hypotheses are added to promising nodes ( both leaves and inner nodes ) of the tree using some randomized method . Theoretical results show that the proposed algorithm asymptotically achieves the performance of the base boosting algorithm applied . Experiments are provided in ranking web documents and move ordering in chess , and the results indicate that the new strategy yields better performance when the length of the sequence is limited , and converges to similar performance as the original boosting algorithms otherwise .
Originally designed for classification tasks , it combines boosting methods and the SMOTE resampling strategy . We present four variants of SMOTEBoost and provide an experimental evaluation using 30 datasets with an extensive analysis of results in order to assess the ability of SMOTEBoost methods in predicting extreme target values , and their predictive trade-off concerning baseline boosting methods . SMOTEBoost is publicly available in a software package .
A bias boosting technique for a 3.2V , 1.9GHz Class AB RF amplifier designed in a 30GHz BiCMOS process is presented in this paper . In a Class AB amplifier , the average current drawn from the supply depends on the input signal level .
The way to combine the global contour templates with local HOGs is by adjusting the bias of a support vector machine ( SVM ) for the local classifier . The method proposed for feature combination is referred to as biased boosting . For covering the human appearance in various poses , an expectation maximization algorithm is used which is a kind of iterative algorithm is used to construct a set of representative weighted contour templates instead of manual annotation .
The Elements of Statistical Learning ” by Hastie , Tibshirani and Friedman . Boosting Boosting relates to the idea of taking a class of weak learners ( meaning , their error is close to 0.5 ) and turning them into a strong classifier ( one that gets arbitrary low error ) . Perhaps the most well-known binary classification algorithm based on boosting is the Adaboost algorithm [ 1 ] ( see Fig. 1 ) .
We first unify a well-known top-down decision tree induction algorithm due to [ Kearns and Mansour , 1999 ] , and discrete AdaBoost [ Freund and Schapire , 1997 ] , as two versions of a same higher-level boosting algorithm . It may be used as the basic building block to devise simple provable boosting algorithms for complex classifiers . We provide one example : the first boosting algorithm for Oblique Decision Trees , an algorithm which turns out to be simpler , faster and significantly more accurate than previous approaches .
In this paper , a novel approach for learning a vision-based hand detection system is introduced . The main contribution of this paper includes robust boosting-based framework for real-time detection of a hand in unconstrained and heterogeneous environments . The proposed system makes use of efficient representative features which allows fast computation while changing the hand appearances and background .
Using adaptation techniques , we created hidden Markov models for inaudible speech and we performed automatic speech recognition . The achieved results are very promising , and prove the effectiveness of NAM microphone In this paper , we also introduce our work for recognizing normal speech using NAM microphone . The idea is to take advantage of noise robustness of NAM microphone .
This stream-based data representation enables area-power efficient computing units such as the multiplier implemented as an AND gate yielding savings in power of 90 % compared with digital approaches . However , a major bottleneck for computing on streams , vision sensors , and NAM approaches is the cost of analog-to-digital ( ADC ) and digital-to-stream-to-digital converters . Our NAM-ACS architecture , simplifies the sensor and eliminates the need for the expensive conversions .
Communication of the amount of uncertainty , both in the input and output for NAMs , often involves a confidence or prediction interval around a given potency estimate , derived from an understanding of the variability in the data modeled . Tuning expectations of NAM performance to an understanding of the reproducibility and variability , both of traditional approaches and NAM approaches , provides a path for the adoption of NAMs as alternatives in screening chemicals for risk .
Therefore , in this paper , a framework called deep normalized convolutional neural network ( DNCNN ) is proposed for imbalanced fault classification of machinery to overcome the first weakness . Meanwhile , neuron activation maximization ( NAM ) algorithm is developed to handle the second weakness . To verify the proposed methods , three bearing datasets containing single faults and compound faults are constructed with different imbalanced degrees .
The total area comprises 10 fields and 4 remaining prospects and is planned to be fully developed by the year 2001 , using wet gas pipelines to route the production to either the Anjum LTS installation or the Grijpskerk SilicaGel installation . The Rotliegend reservoirs in this part of the Netherlands are very heterogeneous and require a more detailed subsurface simulation than feasible with the standard NAM tool for gas field development ( GENREM ) . In addition , the area is close to the Waddenzee and based on extensive ecological research , NAM uses a stringent , self-imposed ecological constraint , whilst evaluating the development plans for this area .
In this paper , we propose a new deep learning approach , called neural association model ( NAM ) , for probabilistic reasoning in artificial intelligence . We propose to use neural networks to model association between any two events in a domain .
The Saco-Casco Bays in the southern Maine are susceptible to coastal inundation due to the influence of winter storms . The high-resolution Saco-Casco Bays storm surge modeling is setup based on Finite Volume Coastal Ocean Model ( FVCOM ) , one-way nested with the Northeast Coastal Ocean Forecast System ( NECOFS ) and driven by high-resolution NECOFS/WRF and moderate-resolution NAM WRF to understand the impact of wind speed and direction on the predicting skill of the surge . The inundation model calculated a peak surge of more than 0.5 m due to winter storm Juno , at ME8418150 on 27 January 2015 .
The non-symmetry and anti-packing model ( NAM ) is a general pattern representation model that has been developed to help design some efficient image representation methods . In this paper , inspired by the idea of computing moments based on the S-Tree coding ( STC ) representation and by using the NAM and extended shading ( NAMES ) approach , we propose a fast algorithm for computing lower order moments based on the NAMES representation , which takes O(N ) time where N is the number of NAM blocks . By taking three idiomatic standard gray images ‘ Lena ’ , ‘ F16 ’ , and ‘ Peppers ’ in the field of image processing as typical test objects , and by comparing our proposed algorithm with the conventional algorithm and the popular STC representation algorithm for computing the lower order moments , the theoretical and experimental results presented in this paper show that the average execution time improvement ratios of the proposed NAMES approach over the STC approach , and also the conventional approach are 26.63 % , and 82.57 % respectively while maintaining the image quality .
Is It Essential and Is It Safe ? . Copyright : © 2016 Eskin NAM . This is an open-access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use , distribution , and reproduction in any medium , provided the original author and source are credited .
Generation Using NAM Expansion . A new generation method of the Tow-Thomas ( TT ) circuit based on the nodal admittance matrix ( NAM ) expansion is given .
AUCs of all methods for all classes were above 0.92 . Conclusions Neural network models ( CNN & NAM ) generally had higher accuracies compared to the non-neural models ( SVM ) and have a range of accuracies that comparable to the inter-annotator agreement of three neuroradiologists . The NAM method adds ability to hold the algorithm accountable for its classification via heat map generation , thereby adding an auditing feature to this neural network .
Evaluating machine learning and statistical prediction techniques for landslide susceptibility modeling Analyzing dynamic phonetic data using generalized additive mixed modeling: A tutorial focusing on articulatory differences between L1 and L2 speakers of English Generalized additive models for conditional dependence structures Approximating Unknown Mappings: An Experimental Evaluation Trajectory-based differential expression analysis for single-cell sequencing data Bias detection and correction in RNA-Sequencing data Estimating denning date of wolves with daily movement and GPS location fix failure Significance Tests for Boosted Location and Scale Models with Linear Base-Learners Fuzzy preference ranking of multi-objective particle swarm optimization algorithm What do we gain from simplicity versus complexity in species distribution models A Learning Approach Towards Detection and Tracking of Lane Markings Boosting in the Presence of Outliers: Adaptive Classification With Nonconvex Loss Functions Grinding wheel condition monitoring with boosted classifiers A gradient descent boosting spectrum modeling method based on back interval partial least squares Raising the Bar: Stacked Conservative Error Correction Beyond Boosting BoostingTree: parallel selection of weak learners in boosting, with application to ranking SMOTEBoost for Regression: Improving the Prediction of Extreme Values Bias boosting technique for a 1.9GHz class AB RF amplifier Combining Weighted Contour Templates with HOGs for Human Detection Using Biased Boosting Classifiers with Low Decision-Making Error using Linear Combination of Functions Real Boosting a la Carte with an Application to Boosting Oblique Decision Tree An Active Learning Framework for Human Hand Sign Gestures and Handling Movement Epenthesis Using Enhanced Level Building Approach Audible (normal) speech and inaudible murmur recognition using NAM microphone Towards low-power random forest using asynchronous computing with streams Estimating uncertainty in the context of new approach methodologies for potential use in chemical safety evaluation Deep normalized convolutional neural network for imbalanced fault classification of machinery and its understanding via visualization Integrated gas field development: The Anjum story Probabilistic Reasoning via Deep Learning: Neural Association Models Storm surge modeling of Saco-Casco Bays: A FVCOM based study on winter storm Juno A fast algorithm for computing moments of gray images based on NAM and extended shading approach Chromium: Is It Essential and Is It Safe? History and Progress of the Tow Thomas Bi-Quadratic Filter Part III: Generation Using NAM Expansion Natural Language Processing for Classification of Acute, Communicable Findings on Unstructured Head CT Reports: Comparison of Neural Network and Non-Neural Machine Learning Techniques
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LREC DSAA ISLPED IC-AI IJCAI EUSIPCO IGSC IGARSS
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