We report on an image classification task originated from the video observation of beehives. Biologists desire to have an automatic support to identify so called hygienic bees. For this it is important to know which brood cells are in a stadium of initial opening. To find these cells a prescreening process is necessary which distinguishes between three types of cells. To solve this decision pro...

We describe a system for the CoNLL2005 shared task of Semantic Role Labeling. The system implements a two-layer architecture to first identify the arguments and then to label them for each predicate. The components are implemented as SVM classifiers using libSVM. Features were adapted and tuned for the system, including a reduced set for the identifier classifier. Experiments were conducted to ...

Composite neural networks consist of multilayer networks, in which each layer may use different models of neurons: the classical sigmoidal neuron, the kernel neuron (like radial basis function neurons), the logical neuron, and so on. This section is devoted to supervised composite neural networks and contains three main parts. The first is focused on radial basis function (RBF) networks, as int...

Kernel Machines and Solving Linear Systems ▶ Operate in a high-dimensional, implicit feature space; ▶ Rely on the construction of an n× n Gram matrix K; ▶ Popular kernels: – RBF : k (xi,xj) = σ exp ( − 12d 2 ) ; – Matérn : kv= 3 2 (xi,xj) = σ 2 ( 1 + √ 3d ) exp ( − √ 3d ) ; where d = (xi − xj) Λ (xi − xj). ▶ Involve the solution of linear systems Kz = v; ▶ Cholesky Decomposition: – O(n) space a...

In this paper, we discuss the connection of the kernel versions of the ELM classifier with infinite Single-hidden Layer Feedforward Neural networks and show that the original ELM kernel definition can be adopted for the calculation of the ELM kernel matrix for two of the most common activation functions, i.e., the RBF and the sigmoid functions. In addition, we show that a low-rank decomposition...

In this paper, we explore tensor representations that can compactly capture higher-order 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 r...

Support vector machine (SVM) was the first proposed kernel-based method. It uses a kernel function to transform data from input space into a high-dimensional feature space in which it searches for a separating hyperplane. SVM aims to maximise the generalisation ability that depends on the empirical risk and the complexity of the machine. SVM has been widely adopted in real-world applications in...

A class of nonparametric smoothing kernel methods for image processing and filtering that possess edge-preserving properties is examined. The proposed approach is a nonlinearly modified version of the classical nonparametric regression estimates utilizing the concept of vertical weighting. The method unifies a number of known nonlinear image filtering and denoising algorithms such as bilateral ...

-In this paper, we introduce a new classification kernel by embedding self organized map (SOM) clustering with mixture of radial basis function (RBF) networks. The model’s efficacy is demonstrated in solving a multi-class TIMIT speech recognition problem where the kernel is used to learn the multidimensional cepstral feature vectors to estimate their posterior class probabilities. The tests res...

<span id="docs-internal-guid-10508d4e-7fff-5011-7a0e-441840e858c8"><span>This paper compares the fuzzy kernel k-medoids using radial basis function (RBF) and polynomial in hepatitis classification. These two functions were chosen due to their popularity any kernel-based machine learning method for solving classification task. The dataset then used evaluate performance of both method...