Scaling up Machine Learning: Parallel and Distributed Approaches

by Edited by Ron Bekkerman , Mikhail Bilenko , John Langford

• ISBN13: 9780521192248
• ISBN10: 0521192242
• Format: Hardcover
• Copyright: 2011-12-30
• Publisher: Cambridge University Press

Summary

This book presents an integrated collection of representative approaches for scaling up machine learning and data mining methods on parallel and distributed computing platforms. Demand for parallelizing learning algorithms is highly task-specific: in some settings it is driven by the enormous dataset sizes, in others by model complexity or by real-time performance requirements. Making task-appropriate algorithm and platform choices for large-scale machine learning requires understanding the benefits, trade-offs, and constraints of the available options. Solutions presented in the book cover a range of parallelization platforms from FPGAs and GPUs to multi-core systems and commodity clusters, concurrent programming frameworks including CUDA, MPI, MapReduce, and DryadLINQ, and learning settings (supervised, unsupervised, semi-supervised, and online learning). Extensive coverage of parallelization of boosted trees, SVMs, spectral clustering, belief propagation and other popular learning algorithms and deep dives into several applications make the book equally useful for researchers, students, and practitioners.

Table of Contents

Scaling up machine learning: introduction

Frameworks for Scaling Up Machine Learning:

Mapreduce and its application to massively parallel learning of decision tree ensembles

Large-scale machine learning using DryadLINQ

IBM parallel machine learning toolbox

Uniformly fine-grained data parallel computing for machine learning algorithms

Supervised and Unsupervised Learning Algorithms:

PSVM: parallel support vector machines with incomplete Cholesky Factorization

Massive SVM parallelization using hardware accelerators

Large-scale learning to rank using boosted decision trees Krysta

The transform regression algorithm

Parallel belief propagation in factor graphs

Distributed Gibbs sampling for latent variable models Arthur Asuncion

Large-scale spectral clustering with Mapreduce and MPI

Parallelizing information-theoretic clustering methods

Alternative Learning Settings:

Parallel online learning

Parallel graph-based semi-supervised learning

Distributed transfer learning via cooperative matrix factorization

Parallel large-scale feature selection

Applications:

Large-scale learning for vision with GPUS

Large-scale FPGA-based convolutional networks Clement Farabet

Mining tree structured data on multicore systems

Scalable parallelization of automatic speech recognition

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