Kernel methods
Kernel Methods for Tree Structured Data
... traditional methods in machine learning deal with vectorial information, they require an a priori form of ...data, kernel methods are recognized to have a strong theoretical background and to be ...
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Some Properties of Regularized Kernel Methods
... The problem of learning from examples can be seen as the problem of estimating an unknown functional dependency given only a finite (possibly small) number of instances. The seminal work of Vapnik Vapnik (1988) shows ...
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Kernel methods for short-term spatio-temporal wind prediction
... nonlinear methods for producing short-term spatio-temporal wind speed forecast are ...of kernel methods, a kernel least mean squares algorithm and kernel recursive least squares ...
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Semantic Role Labeling Systems for Arabic using Kernel Methods
... There is a widely held belief in the natural lan- guage and computational linguistics commu- nities that Semantic Role Labeling (SRL) is a significant step toward improving important applications, e.g. question answering ...
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An intelligent system based on kernel methods for crop yield prediction
... The kernel methods are among the most researched subjects within machine-learning community in recent years and have been widely applied to pattern recognition and function approximation ...
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Modelling kernel methods for unsupervised learning of micro array data
... clustering methods, is an important machine learning ...combining kernel methods, capable of both handling the high dimensionality and discovering nonlinear relationships in the data, with the ...
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Learning Multiple Tasks with Kernel Methods
... using kernel methods and regularization. The standard single-task kernel methods, such as support vector machines and regularization networks, are extended to the case of multi-task ...
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Extracting Relations with Integrated Information Using Kernel Methods
... using kernel methods. At each level, kernel functions (or kernels) are developed to represent the syntactic ...composite kernel produced the best performance on this ...
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Kernel Methods for Measuring Independence
... RKHS kernel size approaches zero (no Parzen window estimate is invoked): our discussion of this proof is given in Appendix ...which kernel covariance operator-based functionals measure independence ...
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Universal Consistency of Localized Versions of Regularized Kernel Methods
... Though all of these approaches have been extensively studied on simulated and real-world data and their success has experimentally been shown, only little is known on theoretical properties so far. In this article, it is ...
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Kernel Methods for Minimally Supervised WSD
... complete kernel for ...the kernel methods, in which we give the basis for understanding our ...Exploiting kernel methods, we can define and combine individual kernels representing ...
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Kernel Methods for Activation Energy Prediction
... Various kernel methods have already been developed based on these ...spectrum kernel (Leslie et ...mismatch kernel (Leslie et ...string kernel (Lodhi et ...
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Nonlinear dynamic process monitoring using kernel methods
... on kernel methods is not as straightforward as that of linear techniques and cannot be generalised from the linear approaches due the non- linear relationship between the transformed and the original ...
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Detecting Opinion Polarities using Kernel Methods
... We investigate the application of kernel methods to representing both structural and lexical knowledge for predicting polarity of opinions in consumer product review. We introduce any- gram kernels which ...
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On the Performance of Kernel Methods for Skin Color Segmentation
... one-class kernel methods outperform other existing skin color models in normalized RGB and other color transformations, but again, comprehensive numerical comparisons are not reported, and no comparison, to ...
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Learning a Robust Relevance Model for Search Using Kernel Methods
... Kernel methods, including the famous Support Vector Machines (SVM) (Vapnik, 1995), refer to a class of algorithms in machine learning which can be employed in a variety of tasks such as classification, ...
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Biased Support Vector Machines and Kernel Methods for Intrusion Detection
... In our experiments, we perform 5-class classification using different kernel methods [17]. The (training and testing) data set contains 11982 randomly generated points from the data set representing the ...
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Kernel Methods for Relation Extraction
... applying kernel methods to NLP is their speed. Kernel classifiers are relatively slow compared to feature ...a kernel classifier requires evaluation of numerous kernels whose computational ...
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Bayesian Kernel Methods for Natural Language Processing
... SVMs are a frequentist method: they aim to find an approximation to the exact latent function that explains the data. This is in contrast to Bayesian settings, which define a prior distribution on this function and ...
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Kernel Methods for Collaborative Filtering
... Kernel methods work for turned linear model into a non-linear model by embedding data into much higher dimensional (possibly infinite), implicit feature ...the kernel function K satisfies mercer’s ...
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