introduction to semi-supervised learning.pdf

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Preface

Introduction to Statistical Machine Learning

The Data

Unsupervised Learning

Supervised Learning

Overview of Semi-Supervised Learning

Learning from Both Labeled and Unlabeled Data

How is Semi-Supervised Learning Possible?

Inductive vs. Transductive Semi-Supervised Learning

Caveats

Self-Training Models

Mixture Models and EM

Mixture Models for Supervised Classification

Mixture Models for Semi-Supervised Classification

Optimization with the EM Algorithm*

The Assumptions of Mixture Models

Other Issues in Generative Models

Cluster-then-Label Methods

Co-Training

Two Views of an Instance

Co-Training

The Assumptions of Co-Training

Multiview Learning*

Graph-Based Semi-Supervised Learning

Unlabeled Data as Stepping Stones

The Graph

Mincut

Harmonic Function

Manifold Regularization*

The Assumption of Graph-Based Methods*

Semi-Supervised Support Vector Machines

Support Vector Machines

Semi-Supervised Support Vector Machines*

Entropy Regularization*

The Assumption of S3VMs and Entropy Regularization

Human Semi-Supervised Learning

From Machine Learning to Cognitive Science

Study One: Humans Learn from Unlabeled Test Data

Study Two: Presence of Human Semi-Supervised Learning in a Simple Task

Study Three: Absence of Human Semi-Supervised Learning

Study Three: Absence of Human Semi-Supervised Learning in a Complex Task

Discussions

Theory and Outlook

A Simple PAC Bound for Supervised Learning*

A Simple PAC Bound for Semi-Supervised Learning*

Future Directions of Semi-Supervised Learning

Basic Mathematical Reference

Semi-Supervised Learning Software

Symbols

Biography

Index

Introduction to Semi-Supervised Learning

Synthesis Lectures on Artiﬁcial Intelligence and Machine Learning Editors Ronald J. Brachman, Yahoo! Research Thomas Dietterich, Oregon State University Introduction to Semi-Supervised Learning Xiaojin Zhu and Andrew B. Goldberg 2009 Action Programming Languages Michael Thielscher 2008 Representation Discovery using Harmonic Analysis Sridhar Mahadevan 2008 Essentials of Game Theory: A Concise Multidisciplinary Introduction Kevin Leyton-Brown, Yoav Shoham 2008 A Concise Introduction to Multiagent Systems and Distributed Artiﬁcial Intelligence Nikos Vlassis 2007 Intelligent Autonomous Robotics: A Robot Soccer Case Study Peter Stone 2007

Copyright © 2009 by Morgan & Claypool All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means—electronic, mechanical, photocopy, recording, or any other except for brief quotations in printed reviews, without the prior permission of the publisher. Introduction to Semi-Supervised Learning Xiaojin Zhu and Andrew B. Goldberg www.morganclaypool.com ISBN: 9781598295474 ISBN: 9781598295481 paperback ebook DOI 10.2200/S00196ED1V01Y200906AIM006 A Publication in the Morgan & Claypool Publishers series SYNTHESIS LECTURES ON ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING Lecture #6 Series Editors: Ronald J. Brachman, Yahoo! Research Thomas Dietterich, Oregon State University Series ISSN Synthesis Lectures on Artiﬁcial Intelligence and Machine Learning Print 1939-4608 Electronic 1939-4616

Introduction to Semi-Supervised Learning Xiaojin Zhu and Andrew B. Goldberg University of Wisconsin, Madison SYNTHESIS LECTURES ON ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING #6 CM& Morgan & cLaypool publishers

ABSTRACT Semi-supervised learning is a learning paradigm concerned with the study of how computers and natural systems such as humans learn in the presence of both labeled and unlabeled data.Traditionally, learning has been studied either in the unsupervised paradigm (e.g., clustering, outlier detection) where all the data is unlabeled, or in the supervised paradigm (e.g., classiﬁcation, regression) where all the data is labeled. The goal of semi-supervised learning is to understand how combining labeled and unlabeled data may change the learning behavior, and design algorithms that take advantage of such a combination. Semi-supervised learning is of great interest in machine learning and data mining because it can use readily available unlabeled data to improve supervised learning tasks when the labeled data is scarce or expensive. Semi-supervised learning also shows potential as a quantitative tool to understand human category learning, where most of the input is self-evidently unlabeled. In this introductory book, we present some popular semi-supervised learning models, including self-training, mixture models, co-training and multiview learning, graph-based methods, and semi- supervised support vector machines. For each model, we discuss its basic mathematical formulation. The success of semi-supervised learning depends critically on some underlying assumptions. We emphasize the assumptions made by each model and give counterexamples when appropriate to demonstrate the limitations of the different models. In addition, we discuss semi-supervised learning for cognitive psychology. Finally, we give a computational learning theoretic perspective on semi- supervised learning, and we conclude the book with a brief discussion of open questions in the ﬁeld. KEYWORDS semi-supervised learning, transductive learning, self-training, Gaussian mixture model, expectation maximization (EM), cluster-then-label, co-training, multiview learning, mincut, harmonic function, label propagation, manifold regularization, semi-supervised support vector machines (S3VM), transductive support vector machines (TSVM), en- tropy regularization, human semi-supervised learning

To our parents Yu and Jingquan Susan and Steven Goldberg with much love and gratitude.

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