In many areas of machine learning, pattern recognition, information retrieval and bioinformatics, one is often confronted with the massive high-dimensional dataset, which leads to the curse of dimensionality. The computational complexity of learning machines in high-dimensional feature space is very expensive. In addition, noisy features will reduce the performance of learning algorithms. To solve these
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In many areas of machine learning, pattern recognition, information retrieval and bioinformatics, one is often confronted with the massive high-dimensional dataset, which leads to the curse of dimensionality. The computational complexity of learning machines in high-dimensional feature space is very expensive. In addition, noisy features will reduce the performance of learning algorithms. To solve these
(Read More…)
In many areas of machine learning, pattern recognition, information retrieval and bioinformatics, one is often confronted with the massive high-dimensional dataset, which leads to the curse of dimensionality. The computational complexity of learning machines in high-dimensional feature space is very expensive. In addition, noisy features will reduce the performance of learning algorithms. To solve these
(Read More…)
In many areas of machine learning, pattern recognition, information retrieval and bioinformatics, one is often confronted with the massive high-dimensional dataset, which leads to the curse of dimensionality. The computational complexity of learning machines in high-dimensional feature space is very expensive. In addition, noisy features will reduce the performance of learning algorithms. To solve these
(Read More…)
In many areas of machine learning, pattern recognition, information retrieval and bioinformatics, one is often confronted with the massive high-dimensional dataset, which leads to the curse of dimensionality. The computational complexity of learning machines in high-dimensional feature space is very expensive. In addition, noisy features will reduce the performance of learning algorithms. To solve these
(Read More…)
In many areas of machine learning, pattern recognition, information retrieval and bioinformatics, one is often confronted with the massive high-dimensional dataset, which leads to the curse of dimensionality. The computational complexity of learning machines in high-dimensional feature space is very expensive. In addition, noisy features will reduce the performance of learning algorithms. To solve these
(Read More…)
In many areas of machine learning, pattern recognition, information retrieval and bioinformatics, one is often confronted with the massive high-dimensional dataset, which leads to the curse of dimensionality. The computational complexity of learning machines in high-dimensional feature space is very expensive. In addition, noisy features will reduce the performance of learning algorithms. To solve these
(Read More…)
In many areas of machine learning, pattern recognition, information retrieval and bioinformatics, one is often confronted with the massive high-dimensional dataset, which leads to the curse of dimensionality. The computational complexity of learning machines in high-dimensional feature space is very expensive. In addition, noisy features will reduce the performance of learning algorithms. To solve these
(Read More…)
Lipreading is the technology that uses computer to recognize the lip motion se-quence. It involves pattern recognition, artificial intelligence, image processing, andso on. This paper mainly focuses on the feature extraction, selection and combinationunder the single-visual channel and the main work includes:1. In feature extraction, this paper analyzes the applications of manifold learningin lipreading. Manifold
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Intelligent Maintenance for large equipment has been becoming a new hot spot in fault diagnosis research field. The equipment performance degradation assessment is one of important parts of the Intelligent Maintenance, which is also the foundation of the equipment running states prediction. Generally speaking, the process of the equipment performance degradation can be divided into
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