Kernel Methods Fast Algorithms and Real Life Applications
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منابع مشابه
Impact of linear dimensionality reduction methods on the performance of anomaly detection algorithms in hyperspectral images
Anomaly Detection (AD) has recently become an important application of hyperspectral images analysis. The goal of these algorithms is to find the objects in the image scene which are anomalous in comparison to their surrounding background. One way to improve the performance and runtime of these algorithms is to use Dimensionality Reduction (DR) techniques. This paper evaluates the effect of thr...
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Distance metric has a key role in many machine learning and computer vision algorithms so that choosing an appropriate distance metric has a direct effect on the performance of such algorithms. Recently, distance metric learning using labeled data or other available supervisory information has become a very active research area in machine learning applications. Studies in this area have shown t...
متن کاملRobust weighted kernel logistic regression in imbalanced and rare events data
Recent developments in computing and technology, along with the availability of large amounts of raw data, have contributed to the creation of many effective techniques and algorithms in the fields of pattern recognition and machine learning. The main objectives for developing these algorithms include identifying patterns within the available data or making predictions, or both. Great success h...
متن کاملOn boosting kernel density methods for multivariate data: density estimation and classification
Statistical learning is emerging as a promising field where a number of algorithms from machine learning are interpreted as statistical methods and vice–versa. Due to good practical performance, boosting is one of the most studied machine learning techniques. We propose algorithms for multivariate density estimation and classification. They are generated by using the traditional kernel techniqu...
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تاریخ انتشار 2003