Includes bibliographical references and index

CONTENTS

Part I. Introduction
Part II. A machine toolkit
Part III. Analysis fundamentals
Part IV. Machine strategies

Part I. Introduction -- 1. Prologue -- 1.1. Machines that learn -- some recent history -- 1.2. Twenty canonical questions -- 1.3. Outline of the book -- 1.4. A comment about example datasets -- 1.5. Software -- 2. The landscape of learning machines -- 2.1. Introduction -- 2.2. Types of data for learning machines -- 2.3. Will that be supervised or unsupervised? -- 2.4. An unsupervised example -- 2.5. More lack of supervision -- where are the parents? -- 2.6. Engines, complex and primitive -- 2.7. Model richness means what, exactly? -- 2.8. Membership or probability of membership? -- 2.9. A taxonomy of machines? -- 2.10. A note of caution -- one of many -- 2.11. Highlights from the theory -- 3. A mangle of machines -- 3.1. Introduction -- 3.2. Linear regression -- 3.3. Logistic regression -- 3.4. Linear discriminant -- 3.5. t g t g t g t g t g t g t g t g t g t g t g t g t g t g t g t g t g t g t g t g t g t g t g g t g t g t g t g t g t g t g t g t g t g t g t g t g t g t g t g t g t g t g t g t g t g t g t g t g t g t g t g t g t g t g t g t g t g t g t g t g t g t g t g t g t g t g t g t g t g t g t g t g t g t g t g t g t g t g t g t g t g t g t g t g t g t g t g t g t g t g t g t g t g t g t g t g t g t g t g t g t g t g t g t g t g t

"This book is for anyone who has biomedical data and needs to identify variables that predict an outcome, for two-group outcomes such as tumor/not-tumor, survival/death, or response from treatment. Statistical learning machines are ideally suited to these types of prediction problems, especially if the variables being studied may not meet the assumptions of traditional techniques. Learning machines come from the world of probability and computer science but are not yet widely used in biomedical research."--Publisher's website