AIMC Topic: Support Vector Machine

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Relevance popularity: A term event model based feature selection scheme for text classification.

PloS one
Feature selection is a practical approach for improving the performance of text classification methods by optimizing the feature subsets input to classifiers. In traditional feature selection methods such as information gain and chi-square, the numbe...

An improved chaotic fruit fly optimization based on a mutation strategy for simultaneous feature selection and parameter optimization for SVM and its applications.

PloS one
This paper proposes a new support vector machine (SVM) optimization scheme based on an improved chaotic fly optimization algorithm (FOA) with a mutation strategy to simultaneously perform parameter setting turning for the SVM and feature selection. I...

Adaptive feature selection using v-shaped binary particle swarm optimization.

PloS one
Feature selection is an important preprocessing method in machine learning and data mining. This process can be used not only to reduce the amount of data to be analyzed but also to build models with stronger interpretability based on fewer features....

[Intelligent systems tools in the diagnosis of acute coronary syndromes: A systemic review].

Archivos de cardiologia de Mexico
BACKGROUND: Acute myocardial infarction is the leading cause of non-communicable deaths worldwide. Its diagnosis is a highly complex task, for which modelling through automated methods has been attempted. A systematic review of the literature was per...

Protein fold recognition based on sparse representation based classification.

Artificial intelligence in medicine
Knowledge of protein fold type is critical for determining the protein structure and function. Because of its importance, several computational methods for fold recognition have been proposed. Most of them are based on well-known machine learning tec...

ML2Motif-Reliable extraction of discriminative sequence motifs from learning machines.

PloS one
High prediction accuracies are not the only objective to consider when solving problems using machine learning. Instead, particular scientific applications require some explanation of the learned prediction function. For computational biology, positi...

Evaluating the diagnostic utility of applying a machine learning algorithm to diffusion tensor MRI measures in individuals with major depressive disorder.

Psychiatry research. Neuroimaging
Using MRI to diagnose mental disorders has been a long-term goal. Despite this, the vast majority of prior neuroimaging work has been descriptive rather than predictive. The current study applies support vector machine (SVM) learning to MRI measures ...

Discriminating between HuR and TTP binding sites using the k-spectrum kernel method.

PloS one
BACKGROUND: The RNA binding proteins (RBPs) human antigen R (HuR) and Tristetraprolin (TTP) are known to exhibit competitive binding but have opposing effects on the bound messenger RNA (mRNA). How cells discriminate between the two proteins is an in...