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The top documents tagged [mit press v1]
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Probability theory: (lecture 2 on AMLbook.com) maximum likelihood estimation (MLE) get parameters from data Hoeffding’s inequality (HI) how good are my
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MACHINE LEARNING 8. Clustering. Motivation Based on E ALPAYDIN 2004 Introduction to Machine Learning © The MIT Press (V1.1) 2 Classification problem:
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Reduces time complexity: Less computation Reduces space complexity: Less parameters Simpler models are more robust on small datasets More interpretable;
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Fundamentals of Artificial Neural Networks Chapter 7 in amlbook.com
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Fast algorithm and implementation of dissimilarity self-organizing maps
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MACHINE LEARNING 6. Multivariate Methods 1. Based on E Alpaydın 2004 Introduction to Machine Learning © The MIT Press (V1.1) 2 Motivating Example Loan
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Ensembles—Combining Multiple Learners For Better Accuracy Reading Material Ensembles: 1. (optional)
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Machine Learning CSE 681 CH2 - Supervised Learning
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