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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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Actual Causality

Joseph Y. Halpern.

August 2016; p ublished online May 2017 .

Book. Subjects: Artificial Intelligence. 240 pages.

Causality plays a central role in the way people structure the world; we constantly seek causal explanations for our observations. But what does it even mean that an event C “actually...

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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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Applying the Definitions

Joseph Y. Halpern.

in Actual Causality

August 2016; p ublished online May 2017 .

Chapter. Subjects: Artificial Intelligence. 7010 words.

The book concludes with a summary of the key points made, and a discussion of three applications areas from computer science: causality in databases, causality in program verification, and...

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The Art of Causal Modeling

Joseph Y. Halpern.

in Actual Causality

August 2016; p ublished online May 2017 .

Chapter. Subjects: Artificial Intelligence. 23789 words.

According to the definition of causality considered in the previous two chapters, whether A is a cause of B depends on the model used. A can be the cause of B in one model and not...

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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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