New book: Learning to learn

[Sebastian Thrun]

            L E A R N I N G   T O   L E A R N

            Sebastian Thrun and Lorien Pratt (eds.)
            Kluwer Academic Publishers


Over the past three decades, research on machine learning and data
mining has led to a wide variety of algorithms that induce general
functions from examples. As machine learning is maturing, it has begun
to make the successful transition from academic research to various
practical applications. Generic techniques such as decision trees and
artificial neural networks, for example, are now being used in various
commercial and industrial applications.

Learning to learn is an exciting new research direction within
machine learning. Similar to traditional machine learning algorithms,
the methods described in LEARNING TO LEARN induce general functions
from experience. However, the book investigates algorithms that can
change the way they generalize, i.e., practice the last of learning
itself, and improve on it.

To illustrate the utility of learning to learn, it is worthwhile to
compare machine learning to human learning. Humans encounter a
continual stream of learning tasks. They do not just learn concepts of
motor skills, they also learn bias, i.e., they learn how to
generalize. As a result, humans are often able to generalize correctly
from extremely few examples---often just a single example suffices to
teach us a new thing.

A deeper understanding of computer programs that improve their ability
to learn can have large practical impact on the field of machine
learning and beyond. In recent years, the field has made significant
progress towards a theory of learning to learn along with practical
new algorithms, some of which led to impressive results in real-world
applications.

LEARNING TO LEARN provides a survey of some of the most exciting new
research approaches, written by leading researchers in the field. Its
objective is to investigate the utility and feasibility of computer
programs that can learn how to learn, both from a practical and a
theoretical point of view.



This book is organized into four parts

Part I:     Overview articles, in which basic taxonomies and the
            cognitive foundations for algorithms that "learn
            to learn" are introduced and discussed,

            Chapter 1:  Learning To Learn: Introduction and Overview
                        Sebastian Thrun and Lorien Pratt

            Chapter 2:  A Survey of Connectionist Network Reuse
                        Through Transfer
                        Lorien Pratt and Barbara Jennings

            Chapter 3:  Transfer in Cognition
                        Anthony Robins


Part II:    Prediction/Supervised Learning, in which specific
            algorithms are presented that exploit information
            in multiple learning tasks in the context of supervised learning,

            Chapter 4:  Theoretical Models of Learning to Learn
                        Jonathan Baxter

            Chapter 5:  Multitask Learning
                        Rich Caruana

            Chapter 6:  Making a Low-Dimensional Representation
                        Suitable for Diverse Tasks
                        Nathan Intrator and Shimon Edelman

            Chapter 7:  The Canonical Distortion Measure for
                        Vector Quantization and Function Approximation
                        Jonathan Baxter

            Chapter 8:  Lifelong Learning Algorithms
                        Sebastian Thrun


Part III:   Relatedness, in which the issue of "task relatedness"
            is investigated and algorithms are described that
            selectively transfer knowledge across learning tasks, and

            Chapter 9:  The Parallel Transfer of Task Knowledge
                        Using Dynamic Learning Rates
                        Daniel L. Silver and Robert E. Mercer

            Chapter 10: Clustering Learning Tasks and the
                        Selective Cross-TaskTransfer of Knowledge
                        Sebastian Thrun and Joseph O'Sullivan


Part IV:    Control, in which algorithms specifically
            designed for learning mappings from percepts to actions are
            presented.

            Chapter 11: CHILD: A First Step Towards Continual Learning
                        Mark B. Ring

            Chapter 12: Reinforcement Learning With Self-Modifying
                        Policies
                        Juergen Schmidhuber, Jieyu Zhao, Nicol N. Schraudolph

            Chapter 13: Creating Advice-Taking Reinforcement Learners
                        Richard Maclin and Jude W. Shavlik



All contributions went to a journal-style reviewing process and are of
journal quality (in fact, many of them were previously published in
Machine Learning or Connection Science). The material is suited for
advanced graduate classes in machine learning.

362 pages. More information at

            http://www.cs.cmu.edu/~thrun/papers/thrun.book3.html

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