Kernel based learning
On the Nyström Method for Approximating a Gram Matrix for Improved Kernel-Based Learning
... up kernel-based learning methods, this paper makes several ...Nystr¨om-based kernel methods (Williams and Seeger, 2001; Williams, Rasmussen, Schwaighofer, and Tresp, 2002; Fowlkes, ...
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KeLP: a Kernel based Learning Platform for Natural Language Processing
... both kernel-based learning algo- rithms and kernel functions over generic data representation, ...decouple kernel func- tions and learning algorithms: once a new kernel ...
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KELP: a Kernel-based Learning Platform
... of kernel functions over discrete data, such as strings, trees or graphs and their combination with standard vectorial ...several kernel-based algorithms, e.g., online and batch kernel ...
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Effective use of WordNet Semantics via Kernel Based Learning
... ambiguation side effect. CD has been successfully applied to semantic tagging ((Basili et al., 2004)). As the WN hierarchies for other POS classes (i.e. verb and adjectives) have topological properties dif- ferent from ...
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Universal Kernel-Based Learning with Applications to Regular Languages
... for learning general countable concept classes and ap- plied it to regular ...of learning regular languages, it means that the training string lengths do not affect hypothesis ...
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Kernel Based Learning of Hierarchical Multilabel Classification Models
... Non-hierarchical models can also be tackled with the above described framework, with a few caveats. First, ensuring global consistency of the marginalized dual is more involved as local consis- tency of edge-marginals ...
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UnibucKernel: A kernel based learning method for complex word identification
... RBF kernel provides generally better results than the linear ...RBF kernel. Similarly, with a MAE of 0.0667 for the RBF kernel, we surpass the top system on English WikiNews, which attained a MAE of ...
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Practical Kernel-Based Reinforcement Learning
... We start the paper presenting some background material in Section 2. Then, in Sec- tion 3, we introduce the stochastic-factorization trick, the insight underlying the devel- opment of our algorithm. KBSF itself is ...
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Learning the Kernel Function via Regularization
... Machine Learning, Pattern Recognition and their ...other kernel-based learning algorithms (see, for example, Sch¨olkopf and Smola, 2002; Shawe-Taylor and Cristianini, 2004, and references ...
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Large Scale Multiple Kernel Learning
... classical kernel-based learning algorithms are based on a single kernel, in practice it is often desirable to use multiple ...of kernel matrices for classification, leading to a ...
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Random Feature-based Online Multi-kernel Learning in Environments with Unknown Dynamics
... by kernel meth- ods, several learning tasks are also expected to be performed in an online ...online kernel-based learning methods that inherit the merits of their batch counterparts, ...
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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 ...
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Learning the Kernel Matrix with Semidefinite Programming
... Kernel-based learning algorithms work by embedding the data into a Euclidean space, and then searching for linear relations among the embedded data ...so-called kernel matrix, a symmetric and ...
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Parameter Optimization of Kernel-based One-class Classifier on Imbalance Learning
... Normal classifiers generally perform poorly on im- balance datasets, whether the class distribution fits in category one or two. Most of classifiers are designed to generalize from training data and output the simplest ...
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Application of learning techniques based on kernel methods for the fault diagnosis in industrial processes
... herramientas kernel utilizadas en las tareas de diagn´ ostico de fallos, y los problemas que respecto a su aplicaci´ on pr´ actica, constituyen l´ıneas abiertas a investigaci´ ...
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Auditing Deep Learning processes through Kernel based Explanatory Models
... Table 1 reports question-explanation pairs with similarity estimates based on the adopted CSPTK kernel function. It is clear from the examples that similarity alone is not able to correlate with clas- ...
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A GENERALIZED KERNEL-BASED RANDOM K-SAMPLESETS METHOD FOR TRANSFER LEARNING
... of learning algorithms is that the data used for training and testing are drawn from the same ...a learning algorithm trained on some products for a new product that helps to reduce the effort of annotating ...
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Structured Output Learning with Polynomial Kernel
... the kernel to reduce the computational complexity of learn- ing with restricted structured output and kernels, and pro- pose cost functions to take into account the different class proportions among the problems, ...
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Kernel Optimization Based Enhanced Preference Learning for Online Movie Recommendation
... is based on a collaborative filtering method that uses user-supplied information, analyses them, after that best movies which are recommended to the user at the ...performed based on K-means ...cluster ...
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Estimation of the applicability domain of kernel-based machine learning models for virtual screening
... The kernels that are used in this work describe the similarity between two molecules by considering pair- wise atomic similarities and combining these in different ways to compare the molecular structure. The atoms are ...
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