The focus of the paper is the problem of learning kernel operators from empirical data. We cast the kernel design problem as the construction of an accurate kernel from simple (and less accurate) base kernels. We use the boosting paradigm to perform the kernel construction process. To do so, we modify the booster so as to accommodate kernel operators. We also devise an efficient weak-learner fo...

Abstract In the present study, classical and proposed methods were used to investigate monthly precipitation characteristics of 30 stations in southeastern United States during 1968–2018. Maximal overlap discrete wavelet transform (MODWT) as preprocessing method K-means clustering used. First, time series decomposed into several subseries using MODWT considering db mother wavelet. Then, energy ...

Low-rank approximations are popular methods to reduce the high computational cost of algorithms involving large-scale kernel matrices. The success of low-rank methods hinges on the matrix rank, and in practice, these methods are effective even for high-dimensional datasets. The practical success has elicited the theoretical analysis of the function rank in this paper, which is an upper bound of...

In this paper, Support Vector Regression (SVR) training models using three different kernels: polynomial, Radial Basis Function (RBF), and mixed kernels, are constructed to demonstrate the training performance of unarranged data obtained from 32 virtual 3-D computer models. The 32 samples used as input data for training the three SVR models are represented by the coordination value sets of poin...

A wide interest has been observed in the medical health care applications that interpret neuroimaging scans by machine learning systems. This research proposes an intelligent, automatic, accurate, and robust classification technique to classify the human brain magnetic resonance image (MRI) as normal or abnormal, to cater down the human error during identifying the diseases in brain MRIs. In th...

Metal magnetic memory (MMM) signals can reflect stress concentration and cracks on the surface of ferromagnetic components, but the traditional criteria used to distinguish the locations of these stress concentrations and cracks are not sufficiently accurate. In this study, 22 indices were extracted from the original MMM signals, and the diagnosis results of 4 kernel functions of support vector...

In this paper we present and evaluate a simple but effective machine learning algorithm that we call Bitvector Machine: Feature vectors are partitioned along component-wise quantiles and converted into bitvectors that are learned. It is shown that the method is efficient in both training and classification. The effectiveness of the method is analysed theoretically for best and worst-case scenar...

We propose a simple but efficient method to extract rules from the radial basis function (RBF) neural network. Firstly, the data are classified by an RBF classifier. During training the RBF network, we allow for large overlaps between clusters corresponding to the same class to reduce the number of hidden neurons while maintaining classification accuracy. Secondly, centers of the kernel functio...

Biometric systems identify a person through physical traits or verify his/her identity through automatic processes. Various systems were used over years including systems like fingerprint, iris, facial images, hand geometry and speaker recognition. For biometric systems successful implementation, it has to address issues like efficiency, accuracy, applicability, robustness and universality. Sin...

this paper presents a comparison study between the multilayer perceptron (mlp) and radial basis function (rbf) neural networks with supervised learning and back propagation algorithm to track hand gestures. both networks have two output classes which are hand and face. skin is detected by a regional based algorithm in the image, and then networks are applied on video sequences frame by frame in...