Biclustering algorithms partition data and covariates simultaneously, providing new insights in several domains, such as analyzing gene expression to discover new biological functions. This paper develops a new model-...
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Biclustering algorithms partition data and covariates simultaneously, providing new insights in several domains, such as analyzing gene expression to discover new biological functions. This paper develops a new model-free biclustering algorithm in abstract spaces using the notions of energy distance and the maximum mean discrepancy-two distances between probability distributions capable of handling complex data such as curves or graphs. The proposed method can learn more general and complex cluster shapes than most existing literature approaches, which usually focus on detecting mean and variance differences. Although the biclustering configurations of our approach are constrained to create disjoint structures at the datum and covariate levels, the results are competitive. Our results are similar to state-of-the-art methods in their optimal scenarios, assuming a proper kernel choice, outperforming them when cluster differences are concentrated in higher-order moments. The model's performance has been tested in several situations that involve simulated and real-world datasets. Finally, new theoretical consistency results are established using some tools of the optimal transport theory.
The online kernel adaptive filters are non-linear filters which provide impulse response and are more efficient compared to other kernel algorithms. The performance of kernel adaptive filters depends on dictionary siz...
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The online kernel adaptive filters are non-linear filters which provide impulse response and are more efficient compared to other kernel algorithms. The performance of kernel adaptive filters depends on dictionary size. Here the single-image super-resolution using online kernel adaptive filters is a learning-based method. The algorithm generates a sparser solution for obtaining high-resolution image from a low-resolution image. It finds out a dictionary with most significant set of basis vectors using the spatial similarity among the dictionaries created from the low-resolution and high-resolution image patches in the training set. The dictionary is utilised to generate the high-resolution image. The algorithm is analysed on three different kernel adaptive filters, extended kernel recursive least squares, kernel recursive least squares tracker and naive online regularised risk minimisation algorithm. The performance of the super-resolution method is evaluated on a large number of images and is compared with the state-of-the art non-linear solutions to the super-resolution. The results show a better progress in peak signal-to-noise ratio up to 1.2 dB.
This research effort aimed in the estimation of the water supply for the case of "Germasogeia" mountainous watersheds in Cyprus. The actual target was the development of an c-Regression Support Vector Machin...
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ISBN:
(纸本)9783642158216
This research effort aimed in the estimation of the water supply for the case of "Germasogeia" mountainous watersheds in Cyprus. The actual target was the development of an c-Regression Support Vector Machine (SVMR) system with five input parameters. The 5-Fold Cross Validation method was applied in order to produce a more representative training data set. The fuzzy-weighted SVR combined with a fuzzy partition approach was employed in order to enhance the quality of the results and to offer an optimization approach. The final models that were produced have proven to perform with an error of very low magnitude in the testing phase when first time seen data were used.
This research effort aims in the application of soft computing techniques toward water resources management. More specifically, the target is the development of reliable soft computing models capable of estimating the...
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This research effort aims in the application of soft computing techniques toward water resources management. More specifically, the target is the development of reliable soft computing models capable of estimating the water supply for the case of "Germasogeia" mountainous watersheds in Cyprus. Initially, epsilon-Regression Support Vector Machines (epsilon-RSVM) and fuzzy weighted epsilon-RSVMR models have been developed that accept five input parameters. At the same time, reliable artificial neural networks have been developed to perform the same job. The 5-fold cross validation approach has been employed in order to eliminate bad local behaviors and to produce a more representative training data set. Thus, the fuzzy weighted Support Vector Regression (SVR) combined with the fuzzy partition has been employed in an effort to enhance the quality of the results. Several rational and reliable models have been produced that can enhance the efficiency of water policy designers. (C) 2011 Elsevier Ltd. All rights reserved.
We propose a feature selection method for density estimation with quadratic loss. This method relies on the study of unidimensional approximation models and on the definition of confidence regions for the density than...
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In the note the class of block-oriented dynamic nonlinear systems is considered, in particular, Hammerstein and Wiener systems are investigated. Several algorithms for nonlinear system identification are presented. Th...
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In the note the class of block-oriented dynamic nonlinear systems is considered, in particular, Hammerstein and Wiener systems are investigated. Several algorithms for nonlinear system identification are presented. The algorithms exploit various degrees of prior knowledge - from parametric - to nonparametric. Eventually, a semiparametric algorithm, which shares advantages of both approaches is announced.
A selective sampling algorithm is a learning algorithm for classification that, based on the past observed data, decides whether to ask the label of each new instance to be classified. In this paper, we introduce a ge...
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A selective sampling algorithm is a learning algorithm for classification that, based on the past observed data, decides whether to ask the label of each new instance to be classified. In this paper, we introduce a general technique for turning linear-threshold classification algorithms from the general additive family into randomized selective sampling algorithms. For the most popular algorithms in this family we derive mistake bounds that hold for individual sequences of examples. These bounds show that our semi-supervised algorithms can achieve, on average, the same accuracy as that of their fully supervised counterparts, but using fewer labels. Our theoretical results are corroborated by a number of experiments on real-world textual data. The outcome of these experiments is essentially predicted by our theoretical results: Our selective sampling algorithms tend to perform as well as the algorithms receiving the true label after each classification, while observing in practice substantially fewer labels.
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