Computational Optimizations for Machine Learning

The present book contains the 10 articles finally accepted for publication in the Special Issue “Computational Optimizations for Machine Learning” of the MDPI journal Mathematics, which cover a wide range of topics connected to the theory and applications of machine learning, neural networks and artificial intelligence. These topics include, among others, various types of machine learning classes, such as supervised, unsupervised and reinforcement learning, deep neural networks, convolutional neural networks, GANs, decision trees, linear regression, SVM, K-means clustering, Q-learning, temporal difference, deep adversarial networks and more. It is hoped that the book will be interesting and useful to those developing mathematical algorithms and applications in the domain of artificial intelligence and machine learning as well as for those having the appropriate mathematical background and willing to become familiar with recent advances of machine learning computational optimization mathematics, which has nowadays permeated into almost all sectors of human life and activity

Parallel Bayesian ARTMAP and Its OpenCL Implementation

The Bayesian ARTMAP neural network, introduced by Vigdor and Lerner, is an incremental learning algorithm which can efficiently process massive datasets for classification, regression, and probabilistic inference tasks. We introduce the parallelized version of the BA neural network and implement it in OpenCL. Our implementation runs on both multi-core CPUs and GPUs architectures. We test the Parallel Bayesian ARTMAP on several classification and regression benchmarks focusing on speedup and scalability. In some cases, the parallel BA runs by an order of magnitude faster than the sequential implementation. Our implementation has the potential to scale for OpenCL devices with increasing number of compute units