kernel machines
Learning the Kernel with Hyperkernels (Kernel Machines Section)
... of Kernel Target Alignment (Cristianini et ...the kernel matrix of the combined training and test ...for kernel matrices with constant ...the kernel matrices normalized by their ...the ...
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Fast and Scalable Local Kernel Machines
... We show here that locality is not necessary related to computational inefficiency, but, instead, it can be the key factor to obtain very fast kernel methods without the need to smooth locally the global decision ...
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A Divisive Information Theoretic Feature Clustering Algorithm for Text Classification (Kernel Machines Section)
... To counter high-dimensionality various methods of feature selection have been proposed by Yang and Pedersen (1997), Koller and Sahami (1997) and Chakrabarti et al. (1997). Distributional clus- tering of words has proven ...
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Feature Extraction by Non Parametric Mutual Information Maximization (Kernel Machines Section)
... One well known linear transform for dimensionality reduction is principal component analysis or PCA (Devijver and Kittler, 1982). The transform is derived from eigenvectors corresponding to the largest eigenvalues of the ...
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Variable Selection Using SVM based Criteria (Kernel Machines Section)
... Gaussian kernel parameter σ, degree d of a poly- nomial kernel, slack variables penalization C ) that have to be tuned to achieve the best general- ization ...
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An Introduction to Variable and Feature Selection (Kernel Machines Section)
... Choosing what fraction of the data should be used for training and for validation is an open problem. Many authors resort to using the leave-one-out cross-validation procedure, even though it is known to be a high ...
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Use of the Zero Norm with Linear Models and Kernel Methods (Kernel Machines Section)
... There are a number of other works which aim to minimize the zero-norm in a variety of domains. Fung et al. (2000) use a modification of the algorithm just described to try to use as few kernel functions (training ...
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Dimensionality Reduction via Sparse Support Vector Machines (Kernel Machines Section)
... Variable selection is a search problem, with each state in the search space specifying a subset of the possible attributes of the task. Exhaustive evaluation of all variable subsets is usually intractable. Genetic ...
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JKernelMachines: A Simple Framework for Kernel Machines
... To sum it up, designing well adapted kernel functions is attracting a lot of research in the com- munity. JKernelMachines is dedicated to facilitate the use of such exotic kernels. It is thus not designed as an ...
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On the Algorithmic Implementation of Multiclass Kernel-based Vector Machines
... We have discussed so far the underlying principal and algorithmic issues that arise in the design of multiclass kernel-based vector machines. However, to make the learning algorithm practical for large ...
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Accelerated Kernel CCA plus SVDD: A Threestage Process for Improving Face Recognition
... other kernel machines, the standard KCCA has to store and calculate the kernel matrix K , the size of which is the square of the number of samples, in other words, the computational complexity ...
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FUZZY BASED DETECTION AND SWARM BASED AUTHENTICATED ROUTING IN MANET
... vector machines for ...of kernel functions and the classification error and accuracy are noted in each ...RBF kernel gives the best performance compared to all the other kernels (with different ...
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Differential Search Algorithm based Parametric Optimization of Fuzzy Generalized Eigenvalue Proximal Support Vector Machine
... Support Vector Machine (SVM) is an effective model for many classification problems. However, SVM needs the solution of a quadratic program which require specialized code. In addition, SVM has many parameters, which ...
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Analog Circuit Feasibility Modeling using Support Vector Machine with Efficient Kernel Functions
... The scope of the present work is identification of feasible de- sign space for analog circuits using SVM scheme and evalua- tion of the scheme on two analog circuits- a two stage op-amp and a cascode op-amp. Widths of ...
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A Study on Convolution Kernels for Shallow Statistic Parsing
... Second, SCF improves the polynomial kernel (d = 3), i.e. the current state-of-the-art, of about 3 percent points on PropBank (column SCF·P). This suggests that (a) PAK can mea- sure the similarity between two SCF ...
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A Modified Cosine Similarity based Log Kernel for Support Vector Machines in the Domain of Text Classification
... SVM kernel has been done in the past (Lodhi et ...2013). Kernel (a similarity function which takes two input feature vectors and find out how similar they are) boost the performance of SVM especially when ...
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On a Class of Support Vector Kernels based on Frames in Function Hilbert Spaces
... pre-wavelet kernel to the other two kernels is more ...RBF kernel (51% support vectors), 0.019 for the spline kernel (45% support vectors) and ...pre-wavelet kernel (45% support ...
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Building and Auto-Tuning Computing Kernels: Experimenting with Boast and Starpu in the Gysela Code★
... Abstract. Modeling turbulent transport is a major goal in order to predict confinement performance in a tokamak plasma. The gyrokinetic framework considers a computational domain in five dimensions to look at kinetic ...
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Performance Modeling of Horizontal-shaft Rotary Palm Kernel Cracking Machines
... palm kernel cracking machines were identified and only (12) twelve were measureable and selected for further experimental ...palm kernel cracking ...of machines kernel ...
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Mixed Kernel Twin Support Vector Machines Based on the Shuffled Frog Leaping Algorithm
... the kernel function in ...the kernel function as a starting point, firstly, we propose a mixed kernel function to improve the learning ability and generalization ability of the kernel function ...
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