Results 21 to 30 of about 606,186 (241)
Rate-Distortion Bounds for Kernel-Based Distortion Measures
Entropy, 2017 Kernel methods have been used for turning linear learning algorithms into nonlinear ones. These nonlinear algorithms measure distances between data points by the distance in the kernel-induced feature space.
Kazuho Watanabe
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Distributed, MapReduce-Based Nearest Neighbor and E-Ball Kernel k-Means [PDF]
, 2016 Data clustering is an unsupervised learning task that has found many applications in various scientific fields. The goal is to find subgroups of closely related data samples (clusters) in a set of unlabeled data.
Nikolaidis, Nikolaos +3 more
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Kernel matrix trimming for improved Kernel K-means clustering [PDF]
, 2015 The Kernel k-Means algorithm for clustering extends the classic k-Means clustering algorithm. It uses the kernel trick to implicitly calculate distances on a higher dimensional space, thus overcoming the classic algorithm's inability to handle data that ...
Nikolaidis, Nikolaos +3 more
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EXTRACTING POINT OF INTERESTS FROM MOVEMENT DATA USING KERNEL DENSITY AND WEIGHTED K-MEANS [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2019 Development in spatial data acquisition techniques, facilitate the process of analyzing movement characteristics and removed the lack of spatial data challenge.
M. Malekzadeh +2 more
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Robust ASR using Support Vector Machines [PDF]
, 2007 The improved theoretical properties of Support Vector Machines with respect to other machine learning alternatives due to their max-margin training paradigm have led us to suggest them as a good technique for robust speech recognition. However, important
A. Gallardo-Antolín +27 more
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Remote Sensing, 2018 Considering kernels in Convolutional Neural Networks (CNNs) as detectors for local patterns, K-means neural network proposes to cluster local patches extracted from training images and then fixate those kernels as the representative patches in each ...
Chen Ding +4 more
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Object classification using X-ray images
Journal of Computer Sciences Institute, 2020 The main aim of the presented research was to assess the possibility of utilizing geometric features in object classification. Studies were conducted using X-ray images of kernels belonging to three different wheat varieties: Kama, Canadian and Rosa.
Piotr Nowosad, Małgorzata Charytanowicz
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IEEE Access, 2021 Solar radiation intensity is intermittent and uncertain under the influence of meteorological conditions. Clustering them and obtaining high-precision and reliable probabilistic forecasting results play a vital role in the planning and management of ...
Zhendong Zhang +6 more
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Predicting clinical outcomes in neuroblastoma with genomic data integration
Biology Direct, 2018 Background Neuroblastoma is a heterogeneous disease with diverse clinical outcomes. Current risk group models require improvement as patients within the same risk group can still show variable prognosis.
Ilyes Baali +4 more
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K-means algorithms for functional data [PDF]
, 2015 Cluster analysis of functional data considers that the objects on which you want to perform a taxonomy are functions f : X e Rp ↦R and the available information about each object is a sample in a finite set of points f ¼ fðx ; y ÞA X x Rgn ...
García-Rodenas, Ricardo +2 more
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