Disease diagnosis is conducted with a machine learning method. We have proposed a novel machine learning method that hybridizes support vector machine (SVM), particle swarm optimization (PSO), and cuckoo search (CS). The new method consists of two stages: firstly, a CS based approach for parameter optimization of SVM is developed to find the better initial parameters of kernel function, and the...

Protein is an important component of milk powder. The fast and non-destructive detection of protein content in milk powder is important. Infrared spectroscopy technique was applied to achieve this purpose. Least-squares support vector machine (LS-SVM) was applied to building the protein prediction model based on spectral transmission rate. The determination coefficient for prediction (R2pÞ was ...

A new hybrid method called GA-SVM was proposed which combines GA (Genetic Algorithm) as a feature selection model and SVM (Support Vector Machine) as a regression model. With some modifications to the general GA and SVM models, this method can implement feature selection and activity prediction simultaneously, and its performance can be improved. Two experiments are carried out which indicate i...

This paper presents results of applying a machine learning technique, the Support Vector Machine (SVM), to the astronomical problem of matching the Infra-Red Astronomical Satellite (IRAS) and Sloan Digital Sky Survey (SDSS) object catalogues. In this study, the IRAS catalogue has much larger positional uncertainties than those of the SDSS. A model was constructed by applying the supervised lear...

In the present work, the support vector machine (SVM) and Adaboost-SVM have been used to develop a classification model as a potential screening mechanism for a novel series of 5-HT(1A) selective ligands. Each compound is represented by calculated structural descriptors that encode topological features. The particle swarm optimization (PSO) and the stepwise multiple linear regression (Stepwise-...