Connectionists: Book announcement: Predicting Structured Data

[Gökhan Hasan BakIr]

 Dear colleagues,

 hereby I would like to announce a new book on

 Predicting Structured Data,
 MIT Press, 2007.

 This book summarizes developments in a new domain of machine learning,
 in which the prediction must satisfy the additional constraints found
in structured data
 and poses one of machine learning greatest challenges: learning functional
 dependencies between arbitrary input and output domains. This volume
 presents and analyzes the state of the art in machine learning
 algorithms and theory in this novel field. The contributors discuss
 applications as diverse as machine translation, document markup,
 computational biology, and information extraction, among others,
 providing a timely overview of an exciting field.

 The book contains three introductory chapters in the beginning and is
accompanied
 by selected chapters summarizing the current state of the art.

 Table of contents:

 I Introduction

 1 Measuring Similarity with Kernels
 2 Discriminative Models
 3 Modeling Structure via Graphical Models

 II  Structured Prediction Based on Discriminative Models

 4 Joint Kernel Maps
 5 Support Vector Machine Learning for Interdependent and Structured
Output Spaces
 6 Efficient Algorithms for Max-Margin Structured Classification
 7 Discriminative Learning of Prediction Suffix Trees with the
Perceptron Algorithm
 8 A General Regression Framework for Learning String-to-String Mappings
 9 Learning as Search Optimization
 10 Energy-Based Models
 11 Generalization Bounds and Consistency for Structured Labeling

 III Structured Prediction Using Probabilistic Models

 12  Kernel Conditional Graphical Models
 13  Density Estimation of Structured Outputs in Reproducing Kernel
Hilbert Spaces
 14  Gaussian Process Belief Propagation


 For more information, please visit http://mitpress.mit.edu/0262026171/


 Apologies if you get this mail more than once.


Best regards,

Goekhan Bakir

Google Switzerland GmbH