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The Conformal Predictions framework is a recent development in machine learning to associate reliable measures of confidence with results in classification and regression. This framework is founded on the principles of algorithmic randomness (Kolmogorov complexity), transductive inference and hypothesis testing. While the formulation of the framework guarantees validity, the efficiency of the framework...
Recent approaches of graph comparison consider graphs as sets of paths. Kernels on graphs are then computed from kernels on paths. A common strategy for graph retrieval is to perform pairwise comparisons. In this paper, we propose to follow a different strategy, where we collect a set of paths into a dictionary, and then project each graph to this dictionary. Then, graphs can be classified using powerful...
Error-Correcting Output Codes (ECOC) with sub-classes reveal a common way to solve multi-class classification problems. According to this approach, a multi-class problem is decomposed into several binary ones based on the maximization of the mutual information (MI) between the classes and their respective labels. The MI is modelled through the fast quadratic mutual information (FQMI) procedure. However,...
Least Squares Support Vector Machine (LS-SVM) is a classic algorithm for regression estimation and classification. But unfortunately, for really large problems, LS-SVM can become highly memory and time consuming. In this paper, we present a simplified algorithm for LS-SVM, called ILS-SVM, which effectively reduces the algorithmic complexity. In order to improve the rate of convergence and overcome...
This paper addressed multivariate calibration based on least square support vector machines (LS-SVM) regression to provide a powerful model for machine learning and data mining. LS-SVM technique have the advantages to provide the capability of learning a high dimensional feature with fewer training data, and to decrease the computational complexity for requiring only solving a set of linear equation...
This paper presents an adaptive capability control technology to reduce training data in channel equalization, and designs a new space-time equalizer based on structure risk minimum criterion (SRM-STE). Simulation is carried out and the results are compared with a typical recursive least squares space-time equalizer (RLS-STE). It is concluded that SRM-STE has better tracking performance with much...
The efficient selection of predictive and accurate gene subsets for cell-type classification is nowadays a crucial problem in Microarray data analysis. The application and combination of dedicated computational intelligence methods holds a great promise for tackling the feature selection and classification. In this work we present a Differential Evolution (DE) approach for the efficient automated...
Visual category recognition (VCR) is one of the most important tasks in image and video indexing. Spectral methods have recently emerged as a powerful tool for dimensionality reduction and manifold learning. Recently, Spectral Regression combined with Kernel Discriminant Analysis (SR-KDA) has been successful in many classification problems. In this paper, we adopt this solution to VCR and demonstrate...
The use of Mercer kernel methods in statistical learning theory provides for strong learning capabilities, as seen in kernel principal component analysis and support vector machines. Unfortunately the computational complexity of the resulting method is of the order of the training set, which is quite large for many applications. This paper proposes a two step procedure for arriving at a compact and...
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