A Supervised Manifold Learning Method
Zuojin Li, Weiren Shi, Xin Shi, Zhi Zhong (2009)
Computer Science and Information Systems
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Displaying similar documents to “Locally linear discriminate embedding for face recognition.”
A Supervised Manifold Learning Method
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Effective Semi-supervised Nonlinear Dimensionality Reduction for Wood Defects Recognition
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Comparison of speaker dependent and speaker independent emotion recognition
Jan Rybka, Artur Janicki (2013)
International Journal of Applied Mathematics and Computer Science
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This paper describes a study of emotion recognition based on speech analysis. The introduction to the theory contains a review of emotion inventories used in various studies of emotion recognition as well as the speech corpora applied, methods of speech parametrization, and the most commonly employed classification algorithms. In the current study the EMO-DB speech corpus and three selected classifiers, the k-Nearest Neighbor (k-NN), the Artificial Neural Network (ANN) and Support Vector...
Eigenvector weighting function in face recognition.
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Rough sets methods in feature reduction and classification
Roman Świniarski (2001)
International Journal of Applied Mathematics and Computer Science
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The paper presents an application of rough sets and statistical methods to feature reduction and pattern recognition. The presented description of rough sets theory emphasizes the role of rough sets reducts in feature selection and data reduction in pattern recognition. The overview of methods of feature selection emphasizes feature selection criteria, including rough set-based methods. The paper also contains a description of the algorithm for feature selection and reduction based on...
A new approach to multiple class pattern classification with random matrices.
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A simplex trained neural network-based architecture for sensor fusion and tracking of target maneuvers
Yee Chin Wong, Malur K. Sundareshan (1999)
Kybernetika
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One of the major applications for which neural network-based methods are being successfully employed is in the design of intelligent integrated processing architectures that efficiently implement sensor fusion operations. In this paper we shall present a novel scheme for developing fused decisions for surveillance and tracking in typical multi-sensor environments characterized by the disparity in the data streams arriving from various sensors. This scheme employs an integration of a...