03273nam a22004335i 4500001001800000003000900018005001700027007001500044008004100059020003700100024003500137050001400172072001700186072002300203072001600226100007800242245006700320250001800387264007200405300006400477336002600541337002600567338003600593347002400629505042800653520140701081650002902488650002902517650003302546710003402579773002602613776003602639776003602675776003602711856004602747912001402793912001502807999001702822978-981-99-3917-6DE-He21320251030110848.0cr nn 008mamaa231206s2024    si |    s    |||| 0|eng d  a97898199391769978-981-99-3917-67 a10.1007/978-981-99-3917-62doi 4aQ325.5-.7 7aUYQM2bicssc 7aMAT0290002bisacsh 7aUYQM2thema1 aLi, Hang.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut96799010aMachine Learning Methodsh[electronic resource] /cby Hang Li.  a1st ed. 2024. 1aSingapore :bSpringer Nature Singapore :bImprint: Springer,c2024.  aXV, 532 p. 109 illus., 5 illus. in color.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda0 aChapter 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.  aThis 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. 0aMachine learning.95505914aMachine Learning.95505924aStatistical Learning.9679912 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z978981993916908iPrinted edition:z978981993918308iPrinted edition:z978981993919040uhttps://doi.org/10.1007/978-981-99-3917-6  aZDB-2-SCS  aZDB-2-SXCS  c56750d56750