Machine Learning

  • James T. Kwok
  • Zhi-Hua Zhou
  • Lei Xu


This tutorial provides a brief overview of a number of important tools that form the crux of the modern machine learning toolbox. These tools can be used for supervised learning, unsupervised learning, reinforcement learning and their numerous variants developed over the years. Because of the lack of space, this survey is not intended to be comprehensive. Interested readers are referred to conference proceedings such as Neural Information Processing Systems (NIPS ) and the International Conference on Machine Learning (ICML) for the most recent advances.


Independent Component Analysis Markov Decision Process Unlabeled Data Ensemble Method Base Learner 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Bayesian information criterion


Bayes model averaging


binary matrix factorization


Bayesian Yin-Yang


de-correlated component analysis


expectation maximization


factor analysis


hidden Markov model


Hough transform


independent component analysis


International Conference on Machine Learning


independent factor analysis


kernel principal component analysis


linear discriminant analysis


local factor analysis


minor component analysis


minimum description length


Markov decision process


multi-instance learning


multi-instance, multi-label learning


multi-response linear regression


minor subspace analysis


multi-task feature learning


multi-task learning


non-Gaussian factor analysis


neural information processing system


nonnegative matrix factorization


principal component analysis


principal subspace analysis


radial basis function


randomized Hough transform


regularized multi-task learning


rival penalized competitive learning


semi-supervised support vector machine




subspace-based function


state–space model


temporal difference


temporal factor analysis




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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.Dep. Computer Science and EngineeringHong Kong University of Science and TechnologyHong KongHong Kong
  2. 2.National Key Lab. for Novel Software TechnologyNanjing UniversityNanjingChina
  3. 3.Dep. Computer Science and EngineeringThe Chinese University of Hong KongHong KongHong Kong

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