Understanding machine learning : from theory to algorithms / Shai Shalev-Shwartz, the Hebrew University, Jerusalem, Shai Ben-David, University of Waterloo, Canada.

Format:

Book

Author/Creator:

Shalev-Shwartz, Shai.

Contributor:

Ben-David, Shai.

Cambridge University Press.

Harry E. Humphreys Book Fund.

Language:

English

Subjects (All):

Machine learning.

Algorithms.

Physical Description:

1 online resource (xvi, 397 pages) : illustrations

Place of Publication:

New York, NY, USA : Cambridge University Press, 2014.

System Details:

text file

Contents:

Introduction

I. Foundations

A gentle start

A formal learning model

Learning via uniform convergence

The bias-complexity tradeoff

The VC-dimension

Nonuniform learnability

The runtime of learning

II. From Theory to Algorithms

Linear predictors

Boosting

Model selection and validation

Convex learning problems

Regularization and stability

Stochastic gradient descent

Support vector machines

Kernel methods

Multiclass, ranking, and complex prediction problems

Decision trees

Nearest neighbor

Neural networks

III. Additional Learning Models

Online learning

Clustering

Dimensionality reduction

Generative models

Feature selection and generation

IV. Advanced Theory

Rademacher complexities

Covering numbers

Proof of the fundamental theorem of learning theory

Multiclass learnability

Compression bounds

PAC-Bayes.

Notes:

Includes bibliographical references (pages 385-393) and index.

Electronic reproduction. Cambridge Available via World Wide Web.

Description based on print version record.

Local Notes:

Acquired for the Penn Libraries with assistance from the Harry E. Humphreys Book Fund.

ISBN:

9781107298019

1107298016

Publisher Number:

99971243574

Access Restriction:

Restricted for use by site license.