In kernel-based learning algorithms the kernel function determines the scalar product and thereby the metric in the feature space in which the learning algorithm operates. The kernel is usually not adapted by the kernel method itself. Choosing the right kernel function is crucial for the training accuracy and generalization capabilities of the learning machine. It may also influence the runtime...

A novel classification model, named the quantum-behaved particle swarm optimization (QPSO)-based weighted multiple kernel extreme learning machine (QWMK-ELM), is proposed in this paper. Experimental validation is carried out with two different electronic nose (e-nose) datasets. Being different from the existing multiple kernel extreme learning machine (MK-ELM) algorithms, the combination coeffi...

This paper presents ARGOS, the first system that can automatically uncover the semantics of kernel objects directly from a kernel binary. Based on the principle of data use reveals data semantics, it starts from the execution of system calls (i.e., the user level application interface) and exported kernel APIs (i.e., the kernel module development interface), and automatically tracks how an inst...

In this paper a concept of the kernel, i.e., the time critical part of a real time operating system and its dedicated co-processor, especially tailored for embedded applications, are presented. The co-processor acts as a system controller and operates in conjunction with one or more conventional processors in hard real time environments. It is composed of three physically and clearly separated ...

Appropriate selection of the kernel function, which implicitly defines the feature space of an algorithm, has a crucial role in the success of kernel methods. In this paper, we consider the problem of optimizing a kernel function over the class of translation invariant kernels for the task of binary classification. The learning capacity of this class is invariant with respect to rotation and sc...

The prediction of lithology is necessary in all areas of petroleum engineering. This means that to design a project in any branch of petroleum engineering, the lithology must be well known. Support vector machines (SVM’s) use an analytical approach to classification based on statistical learning theory, the principles of structural risk minimization, and empirical risk minimization. In this res...

This work presents kernel functions that can be used in conjunction with the Support Vector Machine – SVM – learning algorithm to solve the automatic text classification task. Initially the Vector Space Model for text processing is presented. According to this model text is seen as a set of vectors in a high dimensional space; then extensions and alternative models are derived, and some preproc...

Many classification problems involve loss functions different from the usual zero-one classification loss. In recent years, several approaches to accommodate loss functions in kernel-based learning algorithms have been suggested, but the construction of kernels has not been motivated by specific loss functions. We propose a method for deriving kernels from probabilistic models, which is tailore...

We present a generalization of the adversarial linear bandits framework, where the underlying losses are kernel functions (with an associated reproducing kernel Hilbert space) rather than linear functions. We study a version of the exponential weights algorithm and bound its regret in this setting. Under conditions on the eigen-decay of the kernel we provide a sharp characterization of the regr...

We introduce a class of analytic positive definite multivariate kernels which includes infinite dot product kernels as sometimes used in machine learning, certain new nonlinearly factorizable kernels and a kernel which is closely related to the Gaussian. Each such kernel reproduces in a certain 'native' Hilbert space of multivariate analytic functions. If functions from this space are interpola...