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Opis

As the power of computing has grown over the past few decades, the field of machine learning has advanced rapidly in both theory and practice. 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 learnings 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.

Szczegóły książki

Kategoria Książki po angielsku Computing & information technology Computer science Artificial intelligence

• Pełny tytuł: Machine Learning in Non-Stationary Environments
• Podtytuł: Introduction to Covariate Shift Adaptation
• Autor: Sugiyama, Masashi (Associate Professor, Tokyo Institute of Technology), Motoaki (ATR Brain Information Communication Research Laboratory Group) Kawanabe
• Język: Angielski
• Oprawa: Twarda
• Liczba stron: 280
• EAN: 9780262017091
• ISBN: 0262017091
• ID: 01375982
• Wydawca: MIT Press Ltd
• Waga: 488 g
• Wymiary: 236 × 150 × 18 mm
• Data wydania: 30. March 2012

Ulubione w innej kategorii

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