The linear least mean squares (LMS) algorithm has been recently extended to a reproducing kernel Hilbert space, resulting in an adaptive filter built from a weighted sum of kernel functions evaluated at each incoming data sample. With time, the size of the filter as well as the computation and memory requirements increase. In this paper, we shall propose a new efficient methodology for constrai...

In this paper, we explore tensor representations that can compactly capture higherorder relationships between skeleton joints for 3D action recognition. We first define RBF kernels on 3D joint sequences, which are then linearized to form kernel descriptors. The higher-order outer-products of these kernel descriptors form our tensor representations. We present two different kernels for action re...

Accurate forecast of rainfall has been one of the most important issues in hydrological research. Due to rainfall forecasting involves a rather complex nonlinear data pattern; there are lots of novel forecasting approaches to improve the forecasting accuracy. In this paper, a new approach using the Modular Radial Basis Function Neural Network (M–RBF–NN) technique is presented to improve rainfal...

Background: The introduction of Kartu Prakerja (Pre-employment Card) Programme, henceforth KPP, which was claimed to have launched in order improve the quality workforce, spurred controversy among members public. discussion covered amount budget, training materials and operations brought out various reactions. Opinions could be largely divided into groups: positive negative sentiments.Objective...

In this paper we propose an improvement for a semi-supervised learning algorithm based on Gaussian random fields and harmonic functions. Semi-supervised functions is graph-based method that uses data point similarity to connect unlabeled points with labeled points, thus achieving label propagation. The proposed concerns the way of determining between two by using hybrid RBF-kNN kernel. This mak...

Commercial detection in news broadcast videos involves judicious selection of meaningful audio-visual feature combinations and efficient classifiers. And, this problem becomes much simpler if these combinations can be learned from the data. To this end, we propose an Multiple Kernel Learning based method for boosting successful kernel functions while ignoring the irrelevant ones. We adopt a int...

The selection of hyper-parameters plays an important role to the performance of least-squares support vector machines (LS-SVMs). In this paper, a novel hyper-parameter selection method for LS-SVMs is presented based on the particle swarm optimization (PSO). The proposed method does not need any priori knowledge on the analytic property of the generalization performance measure and can be used t...

Um als Bildungsanbieter bei gefährdeten Studenten rechtszeitig intervenierend eingreifen zu können, sind Verfahren zur Vorhersage studentischer Leistungen notwendig. Viele Arbeiten haben den Einsatz des SVM-Klassifikators vorgeschlagen. Allerdings wurden unzureichende Angaben zur Wahl eines geeigneten Kernel gegeben. Außerdem kann der SVM-Klassifikator bei fehlenden Trainingsdaten zu allen mögl...

A frequently used method to handle scattered data approximation problems on different structures is the interpolation by linear combinations of a single positive definite kernel. Here the underlying positive definite kernel is responsible for the quality of the common estimates of the two main issues in this approximation process. Firstly the approximation error and secondly the stability of th...

We address the problem of estimating human body pose from a single image with cluttered background. We train multiple local linear regressors for estimating the 3D pose from a feature vector of gradient orientation histograms. Each linear regressor is capable of selecting relevant components of the feature vector depending on pose by training it on a pose cluster which is a subset of the traini...