machine learning task of inferring a function from labelled training data is known as sparse dictionary learning.
machine learning task of inferring a function from labelled training data is known as sparse dictionary learning.
machine learning task of inferring a function from labelled training data is known as?
sparse dictionary learning
machine learning and data mining often employ the same methods and overlap significantly, but while machine learning focuses on prediction, based on "known" properties learned from the training data, data mining focuses on the discovery of (previously) "unknown" properties in the data (this is the analysis step of knowledge discovery in databases). data mining uses many machine learning methods, but with different goals; on the other hand, machine learning also employs data mining methods as "unsupervised learning" or as a preprocessing step to improve learner accuracy. much of the confusion between these two research communities (which do often have separate conferences and separate journals, ecml pkdd being a major exception) comes from the basic assumptions they work with: in machine learning, performance is usually evaluated with respect to the ability to "reproduce known" knowledge, while in knowledge discovery and data mining (kdd) the key task is the discovery of previously "unknown" knowledge. evaluated with respect to known knowledge, an uninformed (unsupervised) method will easily be outperformed by other supervised methods, while in a typical kdd task, supervised methods cannot be used due to the unavailability of training data.
machine learning
machine learning approaches in particular can suffer from different data biases. a machine learning system trained on current customers only may not be able to predict the needs of new customer groups that are not represented in the training data. when trained on man-made data, machine learning is likely to pick up the same constitutional and unconscious biases already present in society. language models learned from data have been shown to contain human-like biases. machine learning systems used for criminal risk assessment have been found to be biased against black people. in 2015, google photos would often tag black people as gorillas, and in 2018 this still was not well resolved, but google reportedly was still using the workaround to remove all gorilla from the training data, and thus was not able to recognize real gorillas at all. similar issues with recognizing non-white people have been found in many other systems. in 2016, microsoft tested a chatbot that learned from twitter, and it quickly picked up racist and sexist language. because of such challenges, the effective use of machine learning may take longer to be adopted in other domains. concern for reducing bias in machine learning and propelling its use for human good is increasingly expressed by artificial intelligence scientists, including fei-fei li, who reminds engineers that "there’s nothing artificial about ai...it’s inspired by people, it’s created by people, and—most importantly—it impacts people. it is a powerful tool we are only just beginning to understand, and that is a profound responsibility.”
machine learning