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A novel sparse kernel density estimator is derived based on a regression approach, which selects a very small subset of significant kernels by means of the D-optimality experimental design criterion using an orthogonal forward selection procedure. The weights of the resulting sparse kernel model are calculated using the multiplicative nonnegative quadratic programming algorithm. The proposed method...
We consider a fully complex-valued radial basis function (RBF) network for regression application. The locally regularised orthogonal least squares (LROLS) algorithm with the D-optimality experimental design, originally derived for constructing parsimonious real-valued RBF network models, is extended to the fully complex-valued RBF network. Like its real-valued counterpart, the proposed algorithm...
The support vector machine has been recently developed for blind equalization of constant modulus signals. In this paper we propose to use a v-support vector regressor (nu-SVR) for blindly equalizing multipath channels because of the high generalization ability of the SVR for short burst sequences. A weighted least square procedure is presented for solving the blind nu-SVR equalizer. The performance...
Computing high-technology manufacturing (HTM) productivity level and growth rate have gained a renewed interest in both growth economists and trade economists. Measuring productivity performance has become an area of concern for companies and policy makers. A novel way about nonlinear regression modeling of high-technology manufacturing (HTM) productivity with the potential support vector machines...
Although supervised learning has been widely used to tackle problems of function approximation and regression estimation, prior knowledge fails to be incorporated into the data-driven approach because the form of input-output data pairs are not applied. To overcome this limitation, focusing on the fusion between rough fuzzy system and very rare samples of input-output pairs with noise, this paper...
Recently, Epsilon-Insensitive Support Vector Regression (epsiv SVR) has been introduced to solve regression and prediction problems. However, the preprocessing of data set and the selection of parameters can become a real computational burden to developer and user. Improper parameters usually lead to prediction performance degradation. In this paper, by introducing Parallel Multidimensional Step Search...
This paper proposes an improved nonlinear canonical correlation analysis algorithm named radial basis function canonical correlation analysis (RBFCCA) for multivariate chaotic time series analysis and prediction. This algorithm follows the key idea of kernel canonical correlation analysis (KCCA) method to make a nonlinear mapping of the original data sets firstly with a RBF network and a linear neural...
Traditional recognition methods which mainly match object images with their skeleton couldnpsilat resolve well complex objectspsila recognition problems. So in the paper, with an introduction and improvement of moment invariants, a new image recognition method is proposed with the combination of skeleton and moment invariants. Firstly, the paper analyses the thoughts of method. Then, the concept of...
This paper presents a novel no reference method to assess image quality. Firstly, the image is divided into many blocks. Textured blocks are selected and their amplitude fall-off curves are employed for quality prediction based on natural scene statistics. Secondly, projections of wavelet coefficients between adjacent scales with the same orientation are utilized to measure the positional similarity...
Gaussian processes is a very promising novel technology that has been applied for both the regression problem and the classification problem. While for the regression problem it yields simple exact solutions, this is not the case for the classification case. The reason is that we encounter intractable integrals. In this paper we propose a new approximate solution for the Gaussian process classification...
Penalized likelihood regression is a concept whereby the log-likelihood of the observations is combined with a term measuring the smoothness of the fit, and the resulting expression is then optimized. This concept vies for achieving a compromise between goodness of fit (as typified by the likelihood function) and smoothness of the data. Penalized likelihood regression, which has been developed in...
Currently, associative neural networks (AsNNs) are among the most extensively studied and understood neural paradigms. In this paper, we use a hybrid model of neural network for associative recall of analog and digital patterns. This hybrid model which consists of self-feedback neural network structures (SFNN) parallel with generalized regression neural network (GRNN) were first proposed by authors...
Accurate rainfall intensity nowcasting has many applications such as flash flood defense and sewer management. Conventional computational intelligence tools do not take into account temporal information, and the series of rainfall is treated as continuous time series. Unfortunately, rainfall intensity is not a continuous time series as it has different dry periods in between raining seasons. Hence,...
In this paper, neural network based ensemble learning methods are introduced in predicting activities of COX-2 inhibitors in Chinese medicine quantitative structure-activity relationship (QSAR) research. Three different ensemble learning methods: bagging, boosting and random subspace are tested using neural networks as basic regression rules. Experiments show that all three methods, especially boosting,...
This study combines wavelet-based feature extractions with kernel partial least square (PLS) regression for international stock index forecasting. Wavelet analysis is utilized as a preprocessing step to decompose and extract most important time scale features from high dimensional input data. Owing to the high dimensionality and heavy multi-collinearity of the input data, a kernel PLS regression model...
This work combines several established regression and meta-learning techniques to give a holistic regression model and presents the proposed learnt topology gating artificial neural networks (LTGANN) model in the context of a general architecture previously published by the authors. The applied regression techniques are artificial neural networks, which are on one hand used as local experts for the...
Gaussian process (GP) model is a flexible nonparametric Bayesian method that is widely used in regression and classification. In this paper we present a probabilistic method where we solve voice activity detection (VAD) and speech enhancement in a single framework of GP regression, modeling clean speech by a GP smoother. Optimized hyperparameters in GP models lead us to a novel VAD method since learned...
Nowadays, huge amounts of information from different industrial processes are stored into databases and companies can improve their production efficiency by mining some new knowledge from this information. However, when these databases becomes too large, it is not efficient to process all the available data with practical data mining applications. As a solution, different approaches for intelligent...
This study used smooth support vector regression and back propagation network as the basic theory in study of the mutual fund performance prediction. This paper used return on performance and return on market to make a comparison, and through the risk values, explored each modelpsilas advantages and disadvantages. This study used Taiwanpsilas equity fund as the prediction target, the validation study...
Naive Bayes for regression (NBR) uses the naive Bayes methodology to numeric prediction tasks. The main reason for its poor performance is the independence assumption. Although many recent researches try to improve the performance of naive Bayes by relaxing the independence assumption, none of them can be directly applied to the regression framework. The objective of this work is to present a new...
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