Graph kernel
A Deep Hybrid Graph Kernel through Deep Learning Networks
... DHGK kernel can outperform the alternative graph kernels, with the exception of the WLSK kernel on the PPIs ...DHGK kernel is still competitive to the WLSK kernel on the PPIis ...DHGK ...
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A shortest-path graph kernel for estimating gene product semantic similarity
... shortest-path graph kernel (spgk) method for calculating the semantic similarity between gene ...a graph, which is an induced subgraph of the GO. Then a graph kernel method is used to ...
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A Graph Kernel based on Jensen-Shannon Representation
... novel graph kernel by aligning the Jensen-Shannon (JS) representations of ...of graph decomposition, the new kernel can be seen as an aligned subgraph kernel that incorporates explicit ...
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A Nested Alignment Graph Kernel through the Dynamic Time Warping Framework
... existing graph kernels is that of decomposing graph- s into substructures and comparing pairs of specific isomorphic ...are graph kernels based on counting pairs of isomorphic a) walks [27], b) paths ...
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Shape classification with a vertex clustering graph kernel
... clustering graph kernel (VCGK) on graph classification ...our kernel with three state-of-the-art kernels, including 1) the Weisfeiler-Lehman subtree kernel (WLSK) [16], 2) the shortest ...
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A quantum Jensen-Shannon graph kernel for unattributed graphs
... each graph being ...quantum graph kernel between the pair of graphs is defined using the quantum Jensen-Shannon divergence between the graph density ...our kernel on several standard ...
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kLogNLP: Graph Kernel–based Relational Learning of Natural Language
... kLog (Frasconi et al., 2012) is a logical and re- lational language for kernel-based learning. It has already proven successful for several tasks in com- puter vision (Antanas et al., 2012; Antanas et al., 2013) ...
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Hierarchical Directed Acyclic Graph Kernel: Methods for Structured Natural Language Data
... Acyclic Graph (HDAG) Kernel” for structured natural language ...HDAG Kernel directly accepts several lev- els of both chunks and their relations, and then efficiently computes the weighed sum of the ...
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A Walk Based Semantically Enriched Tree Kernel Over Distributed Word Representations
... walk-based graph kernel for dependency parse trees that sub- sumes general notions of word-similarity, while focusing on vector representations of words to capture lexical ...
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Study of connectivity properties and network topology for neuroimaging classification by using adaptive Nero fuzzy inference system
... Vector-based kernel is performed on the selected features to measure the similarity of two connectivity networks using local clustering ...The graph kernel only reports the local and global structure ...
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Graph Embedding Using Frequency Filtering
... the graph domain [7], [8], [9]. The graph algorithms of this type come from a classical period of graph based pattern recognition ...namely kernel-based meth- ods and embedding methods. ...
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Shortest Path Graph Kernels for Document Similarity
... a graph kernel between pairs of ...a graph-of-words, we are able to model these relationships and then de- termine how similar two documents are by using a modified shortest-path graph ...
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Kernel Methods for Activation Energy Prediction
... Tanimoto kernel and marginalized graph kernel are both computed based on detection of linear features of ...marginalized kernel is the phenomenon of “tottering”, that is, the kernel ...
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Multiple kernels learning-based biological entity relationship extraction method
... of kernel fusion meth- ods with the three simple kernel methods, we conducted experiments on the BioInfer corpus which is moderate ...separate kernel methods, the tag graph kernel ...
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C-Support Vector Classification the Estimation of the MS Subgroups Classification with Selected Kernels and Parameters
... (RBF) kernel, Poly- nomial kernel, Sigmoid kernel and Linear kernel which are considered as C-SVC algorithms ...C-SVC kernel (Radial Basis Function kernel, Polynomial ...
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Effects of some kernel factors on palm kernel oil extraction using a screw press
... p-value of 0.3110 indicating that the contribution of moisture content (within the range of 3% to 10% w.b.) to changes in PKO yield for samples heated at 130 o C for 10 min was not significant at 5% significance level (p ...
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Effective SAR image segmentation and sea-ice floe distribution analysis via kernel graph cuts based feature extraction and fusion
... To date, the process of developing an automatic algorithm for effective segmentation of SAR Sea-Ice images has not been achievable. As a result, analysis of sea ice images relies on a time consuming expert analysis which ...
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Kernel Mean Shrinkage Estimators
... the kernel function is non-linear, because the estimation happens in the feature space associated with the kernel function rather than in the input ...
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On cutwidth parameterized by vertex cover
... function. Graph layout problems, such as T REEWIDTH , B ANDWIDTH and H AMILTONIAN P ATH are not amenable to “branch- ing” techniques, and hence the design of faster exact exponential time algorithms for these ...
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Forecasting modeling with kernel function integration in gaussian processes
... of Kernel function to fits the interested problem. The Kernel function is stationary (its value do not change by time) (Williams and Rasmussen, 2006;Duvenaud, 2014), high effective and practically applied ...
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