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Download as PDF; Printable version; ... Pages in category "Machine learning algorithms" ... Logic learning machine; LogitBoost;
Machine learning (ML) is a subfield of artificial intelligence within computer science that evolved from the study of pattern recognition and computational learning theory. [1] In 1959, Arthur Samuel defined machine learning as a "field of study that gives computers the ability to learn without being explicitly programmed". [ 2 ]
Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions. [1]
The decision tree is the simplest and most widely used symbolic machine learning algorithm. [100] K-nearest neighbor algorithm was the most widely used analogical AI until the mid-1990s, and Kernel methods such as the support vector machine (SVM) displaced k-nearest neighbor in the 1990s. [ 101 ]
Throughout the book, it is suggested that each different tribe has the potential to contribute to a unifying "master algorithm". Towards the end of the book the author pictures a "master algorithm" in the near future, where machine learning algorithms asymptotically grow to a perfect understanding of how the world and people in it work. [1]
Pioneering machine learning research is conducted using simple algorithms. 1960s: Bayesian methods are introduced for probabilistic inference in machine learning. [1] 1970s 'AI winter' caused by pessimism about machine learning effectiveness. 1980s: Rediscovery of backpropagation causes a resurgence in machine learning research. 1990s
Foundations of Machine Learning. MIT Press, 2018. Chapter 2 contains a detailed treatment of PAC-learnability. Readable through open access from the publisher. D. Haussler. Overview of the Probably Approximately Correct (PAC) Learning Framework. An introduction to the topic. L. Valiant. Probably Approximately Correct. Basic Books, 2013.
Active learning is a special case of machine learning in which a learning algorithm can interactively query a human user (or some other information source), to label new data points with the desired outputs. The human user must possess knowledge/expertise in the problem domain, including the ability to consult/research authoritative sources ...