Machine Learning Methods

Chapter 1 Introduction to Machine learning and Supervised Learning.- Chapter 2 Perceptron.- Chapter 3 K-Nearest-Neighbor.- Chapter 4 The Naïve Bayes Method.- Chapter 5 Decision Tree.- Chapter 6 Logistic Regression and Maximum Entropy Model.- Chapter 7 Support Vector Machine.- Chapter 8 Boosting.- Chapter 9 EM Algorithm and Its Extensions.- Chapter 10 Hidden Markov Model.- Chapter 11 Conditional Random Field.

Hang Li is chief scientist of the Noahs Ark Lab of Huawei Technologies. He is also adjunct professor at Peking University, Nanjing University, Xian Jiaotong University, and Nankai University. His research areas include information retrieval, natural language processing, statistical machine learning, and data mining. He graduated from Kyoto University in 1988 and earned his PhD from the University of Tokyo in 1998. He worked at the NEC lab in Japan during 1991 and 2001. He joined Microsoft Research Asia in 2001 and has been working there until present. Hang has about 100 publications at top international journals and conferences, including SIGIR, WWW, WSDM, ACL, EMNLP, ICML, NIPS, and SIGKDD. He and his colleagues papers received the SIGKDD08 best application paper award and the SIGIR08 best student paper award. Hang has also been working on the development of several products. These include Microsoft SQL Server 2005, Microsoft Office 2007 and Office 2010, Microsoft Live Search 2008, Microsoft Bing 2009 and Bing 2010. He has also been very active in the research communities and served or is serving the top conferences and journals. For example, in 2011, he is PC co-chair of WSDM11; area chairs of SIGIR11, AAAI11, NIPS11; PC members of WWW11, ACL-HLT11, SIGKDD11, ICDM11, EMNLP11; and an editorial board member on both the Journal of the American Society for Information Science and the Journal of Computer Science & Technology.

This book provides a comprehensive and systematic introduction to the principal machine learning methods, covering both supervised and unsupervised learning methods. It discusses essential methods of classification and regression in supervised learning, such as decision trees, perceptrons, support vector machines, maximum entropy models, logistic regression models and multiclass classification, as well as methods applied in supervised learning, like the hidden Markov model and conditional random fields. In the context of unsupervised learning, it examines clustering and other problems as well as methods such as singular value decomposition, principal component analysis and latent semantic analysis.
As a fundamental book on machine learning, it addresses the needs of researchers and students who apply machine learning as an important tool in their research, especially those in fields such as information retrieval, natural language processing and text data mining. In order to understand the concepts and methods discussed, readers are expected to have an elementary knowledge of advanced mathematics, linear algebra and probability statistics. The detailed explanations of basic principles, underlying concepts and algorithms enable readers to grasp basic techniques, while the rigorous mathematical derivations and specific examples included offer valuable insights into machine learning.

Provides introduction to principle machine learning methods, covering both supervised and unsupervised learning methods Presents clear descriptions, detailed proofs, and concrete examples using concise language Written by a leading expert on machine learning and natural language processing