Titlebook: Machine Learning for Adaptive Many-Core Machines - A Practical Approach; Noel Lopes,Bernardete Ribeiro Book 2015 Springer International Pu

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Non-Negative Matrix Factorization (NMF) spaces, effectively reducing the number of features while retaining the basis information necessary to reconstruct the original data. Basically, it decomposes a matrix, containing only non-negative coefficients, into the product of two other non-negative matrices with reduced ranks. Since negative

refraction

Deep Belief Networks (DBNs)t signal data. New insights of the visual cortex and studies in the relations between the connectivity found in the brain and mechanisms for mind inference have enlightened the development of deep neural networks. In this chapter the motivation for the design of these architectures points out toward

ostensible

Adaptive Many-Core Machinesity in mind. The rationale is to increase their practical applicability to largescale ML problems. The common underlying thread has been the recent progress in usability, cost effectiveness and diversity of parallel computing platforms, specifically, Graphics Processing Units (GPUs), tailored for a

错事

Incremental Hypersphere Classifier (IHC)n terms of multi-class support, complexity, scalability and interpretability. The Incremental Hypersphere Classifier (IHC) is tested in well-known benchmarks yielding good classification performance results. Additionally, it can be used as an instance selection method since it preserves class boundary samples.

GROG

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Motivation and Preliminariesons that need to be consistent, well-posed and robust. In the final of the chapter an approach to combine supervised and unsupervised models is given which can impart in better adaptive models in many applications.

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Support Vector Machines (SVMs)derstanding of specific aspects related to the implementation of basic SVM machines in a many-core perspective. Further developments can easily be extended to other SVM variants launching one step further the potential for big data adaptive machines.

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Non-Negative Matrix Factorization (NMF)nce cost function. In addition, a new semi-supervised approach that reduces the computational cost while improving the accuracy of NMF-based models is also presented. Finally, we present results for well-known face recognition benchmarks that demonstrate the advantages of both the proposed method and the GPU implementations.

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