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 an...
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ISBN:
(数字)9789819939176
ISBN:
(纸本)9789819939169;9789819939190
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.
The role of Monte Carlo methods and simulation in all of the sciences has in creased in importance during the past several years. These methods are at the heart of the rapidly developing subdisciplines of comput...
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ISBN:
(数字)9781475729603
The role of Monte Carlo methods and simulation in all of the sciences has in creased in importance during the past several years. These methods are at the heart of the rapidly developing subdisciplines of computational physics, compu tational chemistry, and the other computational sciences. The growing power of computers and the evolving simulation methodology have led to the recog nition of computation as a third approach for advancing the natural sciences, together with theory and traditional experimentation. Monte Carlo is also a fundamental tool of computational statistics. At the kernel of a Monte Carlo or simulation method is random number generation. Generation of random numbers is also at the heart of many standard statis tical methods. The random sampling required in most analyses is usually done by the computer. The computations required in Bayesian analysis have become viable because of Monte Carlo methods. This has led to much wider applications of Bayesian statistics, which, in turn, has led to development of new Monte Carlo methods and to refinement of existing procedures for random number generation.
This book includes high-quality papers presented at 18th International Conference on Information Technology and Applications (ICITA 2024), held in Sydney, Australia, during October 17–19, 2024. The book presents orig...
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ISBN:
(数字)9789819617586
ISBN:
(纸本)9789819617579
This book includes high-quality papers presented at 18th International Conference on Information Technology and Applications (ICITA 2024), held in Sydney, Australia, during October 17–19, 2024. The book presents original research work of academics and industry professionals to exchange their knowledge of the state-of-the-art research and development in information technology and applications. The topics covered in the book are cloud computing, business process engineering, machine learning, evolutionary computing, big data analytics, internet of things and cyber-physical systems, information and knowledge management, computer vision and image processing, computer graphics and games programming, mobile computing, ontology engineering, software and systems modeling, human computer interaction, online learning /e-learning, computer networks, and web engineering.
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