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Machine learning methods are usually based on the assumption that the data generation mechanism does not change over time. Yet real-world applications of machine learning, including image recognition, natural language processing, speech recognition, robot control, and bioinformatics, often violate this common assumption. Dealing with non-stationarity is one of modern machine learning's greatest challenges. This book focuses on a specific non-stationary environment known as covariate shift, in which the distributions of inputs (queries) change but the conditional distribution of outputs (answers) is unchanged, and presents machine learning theory, algorithms, and applications to overcome this variety of non-stationarity.\u003c\/p\u003e\u003cp\u003eAfter reviewing the state-of-the-art research in the field, the authors discuss topics that include learning under covariate shift, model selection, importance estimation, and active learning. They describe such real world applications of covariate shift adaption as brain-computer interface, speaker identification, and age prediction from facial images. With this book, they aim to encourage future research in machine learning, statistics, and engineering that strives to create truly autonomous learning machines able to learn under non-stationarity.\u003c\/p\u003e","brand":"WoB","offers":[{"title":"US \/ VERY_GOOD \/ SBYB","offer_id":50245397086481,"sku":"CIN0262017091VG","price":0.0,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/0262017091.jpg?v=1751324072"},{"product_id":"introduction-to-statistical-machine-learning-book-masashi-sugiyama-phd-9780128021217","title":"Introduction to Statistical Machine Learning","description":"Machine learning allows computers to learn and discern patterns without actually being programmed. When Statistical techniques and machine learning are combined together they are a powerful tool for analysing various kinds of data in many computer science\/engineering areas including, image processing, speech processing, natural language processing, robot control, as well as in fundamental sciences such as biology, medicine, astronomy, physics, and materials.     Introduction to Statistical Machine Learning provides a general introduction to machine learning that covers a wide range of topics concisely and will help you bridge the gap between theory and practice. Part I discusses the fundamental concepts of statistics and probability that are used in describing machine learning algorithms. Part II and Part III explain the two major approaches of machine learning techniques; generative methods and discriminative methods. While Part III provides an in-depth look at advanced topics that play essential roles in making machine learning algorithms more useful in practice. The accompanying MATLAB\/Octave programs provide you with the necessary practical skills needed to accomplish a wide range of data analysis tasks.","brand":"WoB","offers":[{"title":"US \/ GOOD \/ SBYB","offer_id":50344112881937,"sku":"CIN0128021217G","price":0.0,"currency_code":"GBP","in_stock":false},{"title":"GB \/ NEW \/ INGRAM","offer_id":52471969972497,"sku":"NLS9780128021217","price":0.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/0128021217.jpg?v=1751194955"},{"product_id":"statistical-reinforcement-learning-book-masashi-sugiyama-9781439856895","title":"Statistical Reinforcement Learning","description":"Reinforcement learning is a mathematical framework for developing computer agents that can learn an optimal behavior by relating generic reward signals with its past actions. With numerous successful applications in business intelligence, plant control, and gaming, the RL framework is ideal for decision making in unknown environments with large amounts of data.  Supplying an up-to-date and accessible introduction to the field, Statistical Reinforcement Learning: Modern Machine Learning Approaches presents fundamental concepts and practical algorithms of statistical reinforcement learning from the modern machine learning viewpoint. It covers various types of RL approaches, including model-based and model-free approaches, policy iteration, and policy search methods.    Covers the range of reinforcement learning algorithms from a modern perspective Lays out the associated optimization problems for each reinforcement learning scenario covered Provides thought-provoking statistical treatment of reinforcement learning algorithms  The book covers approaches recently introduced in the data mining and machine learning fields to provide a systematic bridge between RL and data mining\/machine learning researchers. It presents state-of-the-art results, including dimensionality reduction in RL and risk-sensitive RL. Numerous illustrative examples are included to help readers understand the intuition and usefulness of reinforcement learning techniques.  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The book provides mathematical theories for density ratio estimation including parametric and non-parametric convergence analysis and numerical stability analysis to complete the first and definitive treatment of the entire framework of density ratio estimation in machine learning.","brand":"WoB","offers":[{"title":"- \/ - \/ -","offer_id":51798396109073,"sku":null,"price":0.0,"currency_code":"GBP","in_stock":true},{"title":"US \/ GOOD \/ SBYB","offer_id":51798396567825,"sku":"CIN1108461735G","price":0.0,"currency_code":"GBP","in_stock":false},{"title":"GB \/ NEW \/ INGRAM","offer_id":52149389066513,"sku":"NLS9781108461733","price":0.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0784\/4072\/6801\/files\/9781108461733.jpg?v=1752171428"},{"product_id":"density-ratio-estimation-in-machine-learning-book-masashi-sugiyama-9780521190176","title":"Density Ratio Estimation in Machine Learning","description":"Machine learning is an interdisciplinary field of science and engineering that studies mathematical theories and practical applications of systems that learn. 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