Relevance Vector Machines and Eponine Models for Genome Sequence Analysis

A Comparative Approximate Economic Behavior Analysis Of Support Vector Machines And Neural Networks Models

ARTS: accurate recognition of transcription starts in human

UNLABELLED We develop new methods for finding transcription start sites (TSS) of RNA Polymerase II binding genes in genomic DNA sequences. Employing Support Vector Machines with advanced sequence kernels, we achieve drastically higher prediction accuracies than state-of-the-art methods. MOTIVATION One of the most important features of genomic DNA are the protein-coding genes. While it is of g...

Relevance Vector Machines for classifying points and regions in biological sequences

The Relevance Vector Machine (RVM) is a recently developed machine learning framework capable of building simple models from large sets of candidate features. Here, we describe a protocol for using the RVM to explore very large numbers of candidate features, and a family of models which apply the power of the RVM to classifying and detecting interesting points and regions in biological sequence...

Application of Artificial Neural Networks and Support Vector Machines for carbonate pores size estimation from 3D seismic data

This paper proposes a method for the prediction of pore size values in hydrocarbon reservoirs using 3D seismic data. To this end, an actual carbonate oil field in the south-western part ofIranwas selected. Taking real geological conditions into account, different models of reservoir were constructed for a range of viable pore size values.  Seismic surveying was performed next on these models. F...

Relevance vector machine and multivariate adaptive regression spline for modelling ultimate capacity of pile foundation

This study examines the capability of the Relevance Vector Machine (RVM) and Multivariate Adaptive Regression Spline (MARS) for prediction of ultimate capacity of driven piles and drilled shafts. RVM is a sparse method for training generalized linear models, while MARS technique is basically an adaptive piece-wise regression approach. In this paper, pile capacity prediction models are developed...