Results 251 to 260 of about 6,366,089 (278)

Learning Invariance Manifolds

Neurocomputing, 1998
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Manifold Learning in Protein Interactomes

Journal of Computational Biology, 2011
Many studies and applications in the post-genomic era have been devoted to analyze complex biological systems by computational inference methods. We propose to apply manifold learning methods to protein-protein interaction networks (PPIN). Despite their popularity in data-intensive applications, these methods have received limited attention in the ...
MARRAS, ELISABETTA, Travaglione A, Capobianco E.   +2 more
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Manifold-Based Learning and Synthesis

IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2009
This paper proposes a new approach to analyze high-dimensional data set using low-dimensional manifold. This manifold-based approach provides a unified formulation for both learning from and synthesis back to the input space. The manifold learning method desires to solve two problems in many existing algorithms.
Dong Huang, Zhang Yi 0001, Xiaorong Pu
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Continuum Isomap for manifold learnings

Computational Statistics & Data Analysis, 2007
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Hongyuan Zha, Zhenyue Zhang
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Learning Manifolds in Forensic Data

2006
Chemical data related to illicit cocaine seizures is analyzed using linear and nonlinear dimensionality reduction methods. The goal is to find relevant features that could guide the data analysis process in chemical drug profiling, a recent field in the crime mapping community. The data has been collected using gas chromatography analysis.
Frédéric Ratle, Anne-Laure Terrettaz-Zufferey, Mikhail F. Kanevski, Pierre Esseiva, Olivier Ribaux   +4 more
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Statistical Learning via Manifold Learning

2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA), 2015
A new geometrically motivated method is proposed for solving the non-linear regression task consisting in constructing a predictive function which estimates an unknown smooth mapping f from q-dimensional inputs to m-dimensional outputs based on a given 'input-output' training pairs. The unknown mapping f determines q-dimensional Regression manifold M(f)
Alexander V. Bernstein, Alexander P. Kuleshov, Yury Yanovich   +2 more
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Manifold Regularized Reinforcement Learning

IEEE Transactions on Neural Networks and Learning Systems, 2018
This paper introduces a novel manifold regularized reinforcement learning scheme for continuous Markov decision processes. Smooth feature representations for value function approximation can be automatically learned using the unsupervised manifold regularization method.
Hongliang Li 0002, Derong Liu 0001, Ding Wang 0001   +2 more
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Piecewise-Linear Manifold Learning

2011
The need to reduce the dimensionality of a dataset whilst retaining inherentmanifold structure is key in many pattern recognition, machine learning andcomputer vision tasks. This process is often referred to as manifold learningsince the structure is preserved during dimensionality reduction by learning theintrinsic low-dimensional manifold that the ...
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Label Distribution Learning by Partitioning Label Distribution Manifold

IEEE Transactions on Neural Networks and Learning Systems, 2023
Xin Geng, Jianhui Lv, Jing Wang
exaly  

Hyperspectral image classification with discriminative manifold broad learning system

Neurocomputing, 2021
Hongfei Lin, Yonghe Chu, Liang Yang
exaly