03468nam a22004815i 4500 978-981-99-3917-6 DE-He213 20251030110848.0 cr nn 008mamaa 231206s2024 si | s |||| 0|eng d 9789819939176 978-981-99-3917-6 10.1007/978-981-99-3917-6 doi Q325.5-.7 UYQM bicssc MAT029000 bisacsh UYQM thema Li, Hang. author. aut http://id.loc.gov/vocabulary/relators/aut 67990 Machine Learning Methods [electronic resource] / by Hang Li. 1st ed. 2024. Singapore : Springer Nature Singapore : Imprint: Springer, 2024. XV, 532 p. 109 illus., 5 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda 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. 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. Machine learning. 55059 Machine Learning. 55059 Statistical Learning. 67991 SpringerLink (Online service) Springer Nature eBook Printed edition: 9789819939169 Printed edition: 9789819939183 Printed edition: 9789819939190 https://doi.org/10.1007/978-981-99-3917-6 ZDB-2-SCS ZDB-2-SXCS 56750 56750