Visual concept detection is one of the most important tasks in image and video indexing. This paper describes our system in the ImageCLEF Visual Concept Detection Task 2010 which ranked first for large-scale visual concept detection tasks in terms of Equal Error Rate (EER) and Area under Curve (AUC) and ranked third in terms of ontology measure. The presented approach involves state-of-the-art ...

In this exploratory paper we study the convergence rates of an iterated method for approximating derivatives of periodic functions using radial basis function (RBF) interpolation. Given a target function sampled on some node set, an approximation of the m derivative is obtained by m successive applications of the operator “interpolate, then differentiate” this process is known in the spline com...

An approach for automatic classification of computed tomography (CT) medical images is presented in this paper. A vast amount of CT images are produced in modern hospitals due to advances of multi-slice Computed Tomography (CT) Scan which handles up to 64 slices per scan. So, an input image based automatic medical image retrieval system is now a necessity. In this paper, Coiflet wavelets are us...

Intrinsically disordered proteins or, regions perform important biological functions through their dynamic conformations during binding. Thus accurate identification of these disordered regions have significant implications in proper annotation of function, induced fold prediction and drug design to combat critical diseases. We introduce DisPredict, a disorder predictor that employs a single su...

Kernel Perceptron is very simple and efficient online classification algorithm. However, it requires increasingly large computational resources with data stream size and is not applicable on large-scale problems or on resource-limited computational devices. In this paper we describe implementation of Kernel Perceptron on ATTiny2313 microcontroller, one of the most primitive computational device...

A self tuning PID control strategy using reinforcement learning is proposed in this paper to deal with the control of wind energy conversion systems (WECS). Actor-Critic learning is used to tune PID parameters in an adaptive way by taking advantage of the model-free and on-line learning properties of reinforcement learning effectively. In order to reduce the demand of storage space and to impro...

In this paper, basing our considerations on kernel-based approaches, we propose a new strategy allowing to approximate the prostate cancer dynamics. In particular, starting from several measurements of a specific biomarker, we estimate the tumor growth rate. To achieve this aim, we pre-process data via Radial Basis Function (RBF) interpolation. A careful choice of the basis function and of its ...

Support vector machine(SVM) is a very popular way to do pattern classification. This paper describes how to implement an support vector machine for face recognition with linear, polynomial and rbf kernel. It also implements principal component analysis and Fisher linear discriminant analysis for dimensionaly reduction before the classification. It implements svm classifier in MATLAB based on li...

The work deals with the application of Support Vector Machines (SVM) for environmental and pollution spatial data analysis and modeling. The main attention is paid to classification of spatially distributed data with SVM and comparison with probabilistic mapping using nonparametric geostatistical model (indicator kriging). SVMs with RBF kernels were used. It is shown that optimal bandwidth of k...

We present a practical way of introducing convolutional structure into Gaussian processes, making them more suited to high-dimensional inputs like images. The main contribution of our work is the construction of an inter-domain inducing point approximation that is well-tailored to the convolutional kernel. This allows us to gain the generalisation benefit of a convolutional kernel, together wit...