An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Fast support vector machine training and classification on graphics processors
Proceedings of the 25th international conference on Machine learning
Matrix factorization and neighbor based algorithms for the netflix prize problem
Proceedings of the 2008 ACM conference on Recommender systems
Statistical Learning and Data Science
Statistical Learning and Data Science
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We show how to optimize a Support Vector Machine and a predictor for Collaborative Filtering with Stochastic Gradient Descent on the GPU, achieving 1.66 to 6-times accelerations compared to a CPU-based implementation. The reference implementations are the Support Vector Machine by Bottou and the BRISMF predictor from the Netflix Prices winning team. Our main idea is to create a hash function of the input data and use it to execute threads in parallel that write on different elements of the parameter vector. We also compare the iterative optimization with a batch gradient descent and an alternating least squares optimization. The predictor is tested against over a hundred million data sets which demonstrates the increasing memory management capabilities of modern GPUs. We make use of matrix as well as float compression to alleviate the memory bottleneck.