Analysis of Hidden Markov Models and Support Vector Machines in Financial Applications

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...

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

Exploiting Support Vector Machines for Speaker Verifi

Hidden Markov Models have been proved to be an efficient way for statistically modeling sequence signals. And the Support Vector Machines seem to be a promising candidate to perform the classification task. A new method combining support vector machine and hidden Markov models is proposed. The output of support vector machines are modified as posterior probability using sigmoid function, and ac...

A Discriminative Framework for Detecting Remote Protein Homologies

A new method for detecting remote protein homologies is introduced and shown to perform well in classifying protein domains by SCOP superfamily. The method is a variant of support vector machines using a new kernel function. The kernel function is derived from a generative statistical model for a protein family, in this case a hidden Markov model. This general approach of combining generative m...

Using the Fisher Kernel Method to Detect Remote Protein Homologies

A new method, called the Fisher kernel method, for detecting remote protein homologies is introduced and shown to perform well in classifying protein domains by SCOP superfamily. The method is a variant of support vector machines using a new kernel function. The kernel function is derived from a hidden Markov model. The general approach of combining generative models like HMMs with discriminati...