Results 211 to 220 of about 214,138 (256)
Random Forests and Kernel Methods [PDF]
IEEE Transactions on Information Theory, 2016 Random forests are ensemble methods which grow trees as base learners and combine their predictions by averaging. Random forests are known for their good practical performance, particularly in high dimensional set-tings. On the theoretical side, several studies highlight the potentially fruitful connection between random forests and kernel methods.
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A Novel Kernel Method for Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005Kernel Methods are algorithms that, by replacing the inner product with an appropriate positive definite function, implicitly perform a nonlinear mapping of the input data into a high-dimensional feature space. In this paper, we present a kernel method for clustering inspired by the classical K-Means algorithm in which each cluster is iteratively ...
CAMASTRA F, VERRI, ALESSANDRO
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Eigenvalues Ratio for Kernel Selection of Kernel Methods
Proceedings of the AAAI Conference on Artificial Intelligence, 2015The selection of kernel function which determines the mapping between the input space and the feature space is of crucial importance to kernel methods. Existing kernel selection approaches commonly use some measures of generalization error, which are usually difficult to estimate and have slow convergence rates.
Yong Liu 0018, Shizhong Liao
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The Reproducing Kernel Method. II
Journal of Mathematical Physics, 1972The explicit solution of the Cauchy problem ∂N/∂t = HN by means of reproducing kernels is obtained under various forms: conformal mapping expansions, Sheffer polynomial expansion, polynomials orthogonal on a family of curves; the convergence is studied for both Szegö and Bergman kernels.
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Kernel methods for melanoma recognition.
Studies in health technology and informatics, 2006Skin cancer is a spreading disease in the western world. Early detection and treatment are crucial for improving the patient survival rate. In this paper we present two algorithms for computer assisted diagnosis of melanomas. The first is the support vector machines algorithm, a state-of-the-art large margin classifier, which has shown remarkable ...
La Torre, Elisabetta +3 more
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2006
Kernel Methods are algorithms that implicitly perform, by replacing the inner product with an appropriate Mercer Kernel, a nonlinear mapping of the input data to a high dimensional Feature Space. In this paper, we describe a Kernel Method for clustering.
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Kernel Methods are algorithms that implicitly perform, by replacing the inner product with an appropriate Mercer Kernel, a nonlinear mapping of the input data to a high dimensional Feature Space. In this paper, we describe a Kernel Method for clustering.
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Predictive Nyström method for kernel methods
Neurocomputing, 2017Nystrm method is a widely used matrix approximation method for scaling up kernel methods, and existing sampling strategies for Nystrm method are proposed to improve the matrix approximation accuracy, but leaving approximation independent of learning, which can result in poor predictive performance of kernel methods.
Lizhong Ding, Shizhong Liao
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2007
During the past decade, a major revolution has taken place in pattern-recognition technology with the introduction of rigorous and powerful mathematical approaches in problem domains previously treated with heuristic and less efficient techniques.
Cristianini, N. +2 more
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During the past decade, a major revolution has taken place in pattern-recognition technology with the introduction of rigorous and powerful mathematical approaches in problem domains previously treated with heuristic and less efficient techniques.
Cristianini, N. +2 more
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Geometry and learning curves of kernel methods with polynomial kernels
Systems and Computers in Japan, 2004AbstractThe properties of learning machines with polynomial kernel classifiers, such as support vector machines or kernel perceptrons, are examined. We first derive the number of effective examples which are related to generalization error. Next, we analyze the average prediction errors of several algorithms and show these errors do not depend on the ...
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The Condition of Kernelizing an Algorithm and an Equivalence Between Kernel Methods
2007For a learning algorithm, especially a linear algorithm, it can usually be extended to its kernel version endowed with the power of extracting non-linear features. In this paper, we explore two key questions in the kernelization of an algorithm. The first is the existence of the kernel version of an algorithm.
WenAn Chen, Hongbin Zhang 0009
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