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Active Learning

Masashi Sugiyama and Motoaki Kawanabe.

in Machine Learning in Non-Stationary Environments

March 2012; p ublished online September 2013 .

Chapter. Subjects: Artificial Intelligence. 11586 words.

This chapter examines the problem of active learning. The goal of active learning is to find the most “informative” training input points so that learning can be successfully achieved from...

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Active Learning with Model Selection

Masashi Sugiyama and Motoaki Kawanabe.

in Machine Learning in Non-Stationary Environments

March 2012; p ublished online September 2013 .

Chapter. Subjects: Artificial Intelligence. 2892 words.

This chapter examines the problem of active learning with model selection. Model selection and active learning are two important challenges for successful learning. A natural desire is to...

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An Adversarial View of Covariate Shift and a Minimax Approach

Globerson Amir, Hui Teo Choon, Smola Alex and Roweis Sam.

in Dataset Shift in Machine Learning

December 2008; p ublished online August 2013 .

Chapter. Subjects: Artificial Intelligence. 7532 words.

This chapter considers an adversarial model where the learning algorithm attempts to construct a predictor that is robust to deletion of features at test time. The problem is formulated as...

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Analysis of Benchmarks

Chapelle Olivier, Schölkopf Bernhard and Zien Alexander.

in Semi-Supervised Learning

September 2006; p ublished online August 2013 .

Chapter. Subjects: Artificial Intelligence. 6657 words.

This chapter assesses the strengths and weaknesses of different semi-supervised learning (SSL) algorithms through inviting the authors of each chapter in this book to apply their algorithms...

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Applications of Active Learning

Masashi Sugiyama and Motoaki Kawanabe.

in Machine Learning in Non-Stationary Environments

March 2012; p ublished online September 2013 .

Chapter. Subjects: Artificial Intelligence. 4877 words.

This chapter describes real-world applications of active learning techniques: sampling policy design in reinforcement learning and wafer alignment in semiconductor exposure apparatus.

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Applications of Covariate Shift Adaptation

Masashi Sugiyama and Motoaki Kawanabe.

in Machine Learning in Non-Stationary Environments

March 2012; p ublished online September 2013 .

Chapter. Subjects: Artificial Intelligence. 15379 words.

This chapter discusses state-of-the-art applications of covariate shift adaptation techniques to various real-world problems. It covers non-stationarity adaptation in brain-computer...

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An Augmented PAC Model for Semi-Supervised Learning

Balcan Maria-Florina and Blum Avrim.

in Semi-Supervised Learning

September 2006; p ublished online August 2013 .

Chapter. Subjects: Artificial Intelligence. 13522 words.

This chapter describes an augmented version of the PAC model, designed with semi-supervised learning in mind, that can be used to help think about the problem of learning from labeled and...

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Author Comments

Shimodaira Hidetoshi, Sugiyama Masashi, Storkey Amos, Gretton Arthur and David Shai-Ben.

in Dataset Shift in Machine Learning

December 2008; p ublished online August 2013 .

Chapter. Subjects: Artificial Intelligence. 2102 words.

This chapter presents comments by the authors about dataset shift in machine learning. Topics covered include covariate shift and misspecification; and whether importance weighting is...

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Automatic Construction of Multilingual Name Dictionaries

Bruno Pouliquen and Ralf Steinberger.

in Learning Machine Translation

November 2008; p ublished online August 2013 .

Chapter. Subjects: Artificial Intelligence. 8222 words.

Machine translation and other natural language processing systems often experience performance loss when processing texts with unknown words, such as proper names. Proper name dictionaries...

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Binary Classification under Sample Selection Bias

Hein Matthias.

in Dataset Shift in Machine Learning

December 2008; p ublished online August 2013 .

Chapter. Subjects: Artificial Intelligence. 12334 words.

This chapter examines the problem of binary classification under sample selection bias from a decision-theoretic perspective. Starting from a derivation of the necessary and sufficient...

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