Predicting Traffic Casualties Using Support Vector Machines with Heuristic Algorithms: A Study Based on Collision Data of Urban Roads

Predicting cardiac arrhythmia on ECG signal using an ensemble of optimal multicore support vector machines

The use of artificial intelligence in the process of diagnosing heart disease has been considered by researchers for many years. In this paper, an efficient method for selecting appropriate features extracted from electrocardiogram (ECG) signals, based on a genetic algorithm for use in an ensemble multi-kernel support vector machine classifiers, each of which is based on an optimized genetic al...

STAGE-DISCHARGE MODELING USING SUPPORT VECTOR MACHINES

Establishment of rating curves are often required by the hydrologists for flow estimates in the streams, rivers etc. Measurement of discharge in a river is a time-consuming, expensive, and difficult process and the conventional approach of regression analysis of stage-discharge relation does not provide encouraging results especially during the floods. P

Predicting Nucleolar Proteins Using Support-Vector Machines

The intra-nuclear organisation of proteins is based on possibly transient interactions with morphologically defined compartments like the nucleolus. The fluidity of trafficking challenges the development of models that accurately identify compartment membership for novel proteins. A growing inventory of nucleolar proteins is here used to train a support-vector machine to recognise sequence feat...

Predicting Bankruptcy with Support Vector Machines

Predicting Time Series with Support Vector Machines

Support Vector Machines are used for time series prediction and compared to radial basis function networks. We make use of two diierent cost functions for Support Vectors: training with (i) an insensitive loss and (ii) Huber's robust loss function and discuss how t o c hoose the regularization parameters in these models. Two applications are considered: data from (a) a noisy (normal and uniform...