[PDF] Top 20 Infinite ensemble learning with support vector machines

... First, we can group these kernels by the distance metrics they use. The stump kernel and the Laplacian kernel uses the one-norm distance between vectors, while the others uses the two-norm distance. An advantage of using ... See full document

... It is worth noting that this approach can be viewed as an alternative way for tuning SVM pa- rameters, using an ensemble instead of a single SVM. From this standpoint recent works proposed to automatically choose ... See full document

... Tjong Kim Sang et al. report that they achieve higher accuracy by applying weighted voting of sys- tems which are trained using distinct chunk rep- resentations and different machine learning algo- rithms, such as ... See full document

... The simplest way to characterize the variability in a texture pattern is by noting the gray-level values of the raw pixels. This set of gray values becomes the feature set on which the classification is based. An ... See full document

... machine learning Naïve Bayes remains a popular (baseline) method for text categorization, the problem of judging documents as belonging to one category or the other (such as spam or legitimate, sports or politics, ... See full document

... machine learning literature, another widely used notion closely related to robustness is the stability, where an algorithm is required to be robust (in the sense that the output function does not change ... See full document

... machine learning, support vector machines (SVMs, also support vector networks[1]) are supervised learning models with associated learning algorithms that analyze ... See full document

... This work is organized as follows: In Section 2 we introduce all basic and advanced notions and discuss the latter. We then explain the main ideas of this work and informally present some auxiliary results such as the ... See full document

... done. Support vector machines are supervised learning model that associated with learning algorithms that used to analyse the data and identify the patterns which is used for ... See full document

... Deep Learning (Convolutional Neural Network, Long Short-Term Memory Network, and Long-Term Recurrent Convolutional Network) ...as Support Vector Machines (SVM) and Neural ...Deep ... See full document

... machine learning techniques to tackle the problem of epilepsy lesion ...Boosting, Support Vector Machines (SVM) and the Nearest Neighbor machine learning methods to train and classify ... See full document

... Abstract Healthcare is a rapidly growing industry in both developed and developing countries. The expanse of technology has facilitated the storage and analysis of the diverse data which the healthcare industry ... See full document

... alignment-free vector representations are distance measures, such as the Euclidean dis- tance and the d2 distance ...feature vector representation in combination with supervised machine learning ... See full document

... UPPORT vector (SV) machines [5], [6] are learning algo- rithms based on maximum margin hyperplanes [4] which make use of an implicit mapping into feature space by using a general kernel function in ... See full document

... Machine Learning Methods for Spam Host ...well-known machines learning technique which are multilayer perceptron neural networks, support vector machines, decision trees, random ... See full document

... some nonlinear methods have therefore been proposed to find the nonlinear structure of the manifold, e.g., Isometric Feature Mapping (ISOMAP) [53], Locally Linear Embedding (LLE) [54, 55], Laplacian Eigenmap [56], ... See full document

... Although SVMs showed a learning capacity, such as the perceptron (Lorena and Carvalho 2003; Martins, 2007), we obtained different results. Possibly, owing to the high complexity of the problem under study, it will ... See full document

... negative. Support Vector Machines (SVM) are the most favored supervised learning method of sentiment classification because of their consistently robust performances in natural language ... See full document

... machine learning approaches try to solve the problem of document classification but working on large data sets of heterogeneous classes is a big ...an ensemble of refined Support Vector ... See full document

... large-scale learning, mainly due to their low training ...scale learning, particularly for low dimensional tasks with many training ...the ensemble approaches offered by ... See full document