Combining Multiple Kernels into One Kernel
Kernel Distribution Embeddings: Universal Kernels, Characteristic Kernels and Kernel Metrics on Distributions
... Formally, a topological vector space (TVS) E is a vector space equipped with a topology that is compatible with its linear structure, in the sense that the addition E × E −→ E and scalar multiplication C × E −→ E become ...
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Multiple Kernel Learning with Fisher kernels for High Frequency Currency Prediction
... 5, one can see that reducing the size of the set from which SimpleMKL chooses its kernels to focus on ones that one would suspect would improve performance, either because full-set SimpleMKL ...
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Combining Multiple Resolutions into Hierarchical Representations for kernel-based Image Classification
... As far as the VHSR image is concerned, the prediction maps are noisy with both single and multiple scales. This is due to the fact that the multiscale features extracted on the VHSR image can no longer serve as ...
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Combining many multiple alignments in one improved alignment
... the multiple sequence alignment problem is of high complexity has led to many different heuristic algorithms attempting to find a solution in what would be considered a reasonable amount of computation time and ...
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Transitions between critical kernels: from the tacnode kernel and critical kernel in the two-matrix model to the Pearcey kernel
... − α 1 separate the four cases. The cases are distinguished by the fact whether 0 is in the support of the measures µ 1 , σ 2 − µ 2 , and µ 3 , or not. turns from two intervals into one interval. On this critical ...
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Combining tree kernels and text embeddings for plagiarism detection
... score. One sentence might plagiarize two (or more) sentences, which would lead to a large drop in similarity and subsequently make detection more ...in one sentence but not the other. The multiple ...
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Learning wind fields with multiple kernels
... presents multiple kernel learning (MKL) regression as an exploratory spatial data analysis and modelling ...dedicated kernels to divide a given task into sub-problems and to treat them separately in ...
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Online learning with (multiple) kernels : a review
... of Kernel Hyperparameters in Online Learning. If linear kernels are being used, the kernel function κ is simply the inner product between data examples, so there is no problem using this form of ...
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Kernel Flows: From learning kernels from data into the abyss
... a kernel. We explore a numerical approximation approach to kernel selection/construction based on the simple premise that a kernel must be good if the number of interpolation points can be halved ...
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Empirical Optimal Kernel for Convex Multiple Kernel Learning
... on Multiple Features Digit Recognition and Breast Cancer Wisconsin data set For MKL, a recent approach is to use a two-stage pro- cedure [5],[15], in which the first stage finds the optimal weights to combine the ...
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Probabilistic multiple kernel learning
... Multiple Kernel Learning (MKL) methods aim at learning an optimal (in a pre- defined model-specific sense such as predictive likelihood or zero-one loss) com- bination of individual base ...that ...
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Absent multiple kernel learning
... into multiple kernel-induced feature spaces, and maximize the minimum of all sample-based ...On one hand, this approach is able to effectively handle the issue of absent ...
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Multiple Kernel Learning Algorithms
... different kernels obtained from heterogeneous informa- tion sources, namely, bag-of-words and graphical representations, on the Reuters-21578 data ...set. Combining these two kernels with positive ...
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UNITOR: Combining Syntactic and Semantic Kernels for Twitter Sentiment Analysis
... a Multiple Kernel ...complex kernel functions, such as the Latent Semantic Kernel and Smoothed Par- tial Tree Kernel, to implicitly integrate syn- tactic and lexical information of ...
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Online Learning with Multiple Operator-valued Kernels
... We consider the problem of learning a vector-valued function f in an online learning setting. The function f is assumed to lie in a reproducing Hilbert space of operator-valued kernels. We describe two online ...
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Tensor Decomposition in Multiple Kernel Learning
... and kernel alignment algorithms [9]. Then Section 3 introduces the kernel learning framework, MKL algorithms and its ...study. One important finding is a novel algorithm for decomposing a tensor of ...
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MKBoost: A framework of multiple kernel boosting
... (c) “Sonar” Figure 8: Evaluation of time cost of stochastic MKBoost algorithms with respect to the sampling decay factor β. we found that when setting β to 1 in the extreme case, the two stochastic algorithms converge to ...
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More Efficiency in Multiple Kernel Learning
... We have compared our algorithm to the SILP ap- proach of Sonnenburg et al. Both algorithms use a SVM dual solver based on an active constraints method written in Matlab. The linear programming involved in the SILP ...
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lp-Norm Multiple Kernel Learning
... different kernels representing the TSS signal (weighted degree with shift), the promoter (spectrum), the 1st exon (spectrum), angles (linear), and energies ...Optimal kernel parameters are determined by ...
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Large Scale Multiple Kernel Learning
... using multiple kernel ...the one in Sonnenburg et ...WD kernel and to d = 8 for the spectrum kernel fixing the SVMs regularization parameter to C = ...A kernel cache of 1GB was ...
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