Results 31 to 40 of about 10,276 (261)
A Soft Measurement Method for Carbon Content of Fly Ash Based on Sparseness Approach for LS-SVM
南方能源建设, 2019 [Introduction] The paper aims to establish a sparseness approach based sample distribution for LS-SVM models to solve the problem of excessive computation in the application of classical iterative shearing sparseness algorithm for the soft measurement ...
ZHANG Dahai +4 more
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SAC-NMF-Driven Graphical Feature Analysis and Applications
Machine Learning and Knowledge Extraction, 2020 Feature analysis is a fundamental research area in computer graphics; meanwhile, meaningful and part-aware feature bases are always demanding. This paper proposes a framework for conducting feature analysis on a three-dimensional (3D) model by ...
Nannan Li +3 more
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Mutation rules and the evolution of sparseness and modularity in biological systems. [PDF]
PLoS ONE, 2013 Biological systems exhibit two structural features on many levels of organization: sparseness, in which only a small fraction of possible interactions between components actually occur; and modularity--the near decomposability of the system into modules ...
Tamar Friedlander +3 more
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Scholarpedia, 2008 The(frequently updated) original version is avalable at http://www.scholarpedia.org/article ...
Peter Földiák, Dominik M. Endres
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Doubly Sparse: Sparse Mixture of Sparse Experts for Efficient Softmax Inference
CoRR, 2019 Computations for the softmax function are significantly expensive when the number of output classes is large. In this paper, we present a novel softmax inference speedup method, Doubly Sparse Softmax (DS-Softmax), that leverages sparse mixture of sparse experts to efficiently retrieve top-k classes. Different from most existing methods that require and
Shun Liao +4 more
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Sparse Activity and Sparse Connectivity in Supervised Learning
J. Mach. Learn. Res., 2013 Sparseness is a useful regularizer for learning in a wide range of applications, in particular in neural networks. This paper proposes a model targeted at classification tasks, where sparse activity and sparse connectivity are used to enhance classification capabilities.
Markus Thom, Günther Palm
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IEEE Access, 2023 Recently, an absolute value inequalities discriminant analysis criterion with robustness and sparseness for supervised dimensionality reduction was studied.
Chun-Na Li +4 more
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Sparse PCA from Sparse Linear Regression
CoRR, 2018 To appear in NeurIPS ...
Bresler, Guy +2 more
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Sparse Ordinal Logistic Regression and Its Application to Brain Decoding
Frontiers in Neuroinformatics, 2018 Brain decoding with multivariate classification and regression has provided a powerful framework for characterizing information encoded in population neural activity.
Emi Satake +4 more
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Cancer Informatics, 2022 Identifying individual mechanisms involved in complex diseases, such as cancer, is essential for precision medicine. Their characterization is particularly challenging due to the unknown relationships of high-dimensional omics data and their inter ...
Sarah-Laure Rincourt +2 more
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