Scaling up Machine Learning: Parallel and

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.

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
Table of Contents provided by Publisher. All Rights Reserved.

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Scaling up Machine Learning: Parallel and Distributed Approaches

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