Methods for training machines to categorize data, and/or recognize patterns in data, and machines and systems so trained. More specifically, variations of the invention relates to methods for training machines that include providing one or more training data samples encompassing one or more data classes, identifying patterns in the one or more training data samples, providing one or more data samples representing one or more unknown classes of data, identifying patterns in the one or more of the data samples of unknown class(es), and predicting one or more classes to which the data samples of unknown class(es) belong by comparing patterns identified in said one or more data samples of unknown class with patterns identified in said one or more training data samples. Also provided are tools, systems, and devices, such as support vector machines (SVMs) and other methods and features, software implementing the methods and features, and computers or other processing devices incorporating and/or running the software, where the methods and features, software, and processors utilize specialized methods to analyze data.
Please help me write a proper abstract based on the patent claims. CLAIM: 1-20. (canceled) 21. A method for weighting trained learning machines comprising: training two or more learning machines using two or more training data sets to obtain two or more trained learning machines; obtaining multiple measures of training output from each of said two or more trained learning machines; training one or more selecting learning machines to select among the two or more trained learning machines based upon said multiple measures of training output; selecting one of said two or more learning machines using said one or more selecting learning machines; and outputting the selected trained learning machine into a computer memory. 22. The method of claim 21, wherein the selected trained learning machine is output on an output device. 23. The method of claim 21, wherein the learning machine is selected from the group consisting of a Support Vector Machine (SVM), and a neural network. 24. The method of claim 21, wherein the selecting learning machines compare performance measures of the trained learning machines and predict querying success. 25. The method of claim 21, wherein the measures of training output are selected from the group consisting of number of features, number of support vectors, number of unbound margins, divergence, margins, angles, positive leave one out, negative leave one out, total leave one out, and stability. 26. The method of claim 23, wherein the selecting learning machine is a neural network. 27. The method of claim 26, wherein the neural network assigns weight to each of said multiple measures of training output and selects from among the multiple trained learning machines based on said weights. 28. The method of claim 27, wherein the neural network assigns weight based on one or more weighting methods selected from the group consisting of LOO, LTO, n-fold cross-validation, number of support vectors chosen, Vapnik-Chervonenkis (VC) dimension, ratio of all support vectors to unbounded support vectors, relative magnitude of the parameters concerned, favoring a large sigma for a Gaussian kernel, small upper bound for Lagrange multipliers of each point, divergence, and combinations thereof. 29. The method of claim 28, wherein the weighting method or methods providing the greatest discrimination among multiple trained machines is selected. 30. A machine learning method, comprising: providing one or more training data samples having one or more known classes; supplementing said training data samples with one or more randomly-generated negative examples having a reduced weight assigned thereto, to obtain one or more supplemented training data samples; training two or more learning machines to identify said one or more known classes using said one or more supplemented training data samples; selecting the trained learning machine that optimizes a performance function dependent on one or more variables between said one or more known classes; and outputting the selected trained learning machine into a computer memory. 31. The method of claim 30, wherein the negative examples are generated by methods selected from the group consisting of estimation of the density function of the positive class, importance-sampling Monte Carlo methods, and random selection from actual data. 32. The method of claim 30, wherein the selected trained learning machine is output on an output device. 33. The method of claim 30, wherein the learning machine is selected from the group consisting of a Support Vector Machine (SVM), a decision tree, a hidden Markov model, a Bayesian network, a Gram Schmidt algorithm, a neural network, and a genetic algorithm 34. The method of claim 30, wherein the learning machine optimizes a performance function selected from the group consisting of LOO, LTO, n-fold cross-validation, number of support vectors chosen, Vapnik-Chervonenkis (VC) dimension, ratio of all support vectors to unbounded support vectors, relative magnitude of the parameters concerned, favoring a large sigma for a Gaussian kernel, small upper bound for Lagrange multipliers of each point, divergence, and combinations thereof.