Statistical Mechanics of Support Vector Networks
نویسندگان
چکیده
Rainer Dietrich,1 Manfred Opper,2 and Haim Sompolinsky3 1Institut für Theoretische Physik, Julius-Maximilians-Universität, Am Hubland, D-97074 Würzburg, Germany 2Department of Computer Science and Applied Mathematics, Aston University, Birmingham B4 7ET, United Kingdom 3Racah Institute of Physics and Center for Neural Computation, Hebrew University, Jerusalem 91904, Israel (Received 30 November 1998)
منابع مشابه
A DWT and SVM based method for rolling element bearing fault diagnosis and its comparison with Artificial Neural Networks
A classification technique using Support Vector Machine (SVM) classifier for detection of rolling element bearing fault is presented here. The SVM was fed from features that were extracted from of vibration signals obtained from experimental setup consisting of rotating driveline that was mounted on rolling element bearings which were run in normal and with artificially faults induced conditio...
متن کاملA Comparative Approximate Economic Behavior Analysis Of Support Vector Machines And Neural Networks Models
متن کامل
بررسی کارایی مدلهای هوشمند در برآورد رسوبات معلق رودخانهای (مطالعه موردی: حوزه آبخیز باباامان، خراسان شمالی)
Accurate estimation of the sediment volume carried by the rivers is important in water related projects and recognition and suggestion proper methods for estimating suspended sediment goals which should be conducted by related researches. Among the methods that have been recently used to model suspended sediment, machine learning based methods such as decision trees, support vector machine, and...
متن کاملPrediction of true critical temperature and pressure of binary hydrocarbon mixtures: A Comparison between the artificial neural networks and the support vector machine
Two main objectives have been considered in this paper: providing a good model to predict the critical temperature and pressure of binary hydrocarbon mixtures, and comparing the efficiency of the artificial neural network algorithms and the support vector regression as two commonly used soft computing methods. In order to have a fair comparison and to achieve the highest efficiency, a comprehen...
متن کاملApplication 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...
متن کاملPermeability estimation from the joint use of stoneley wave velocity and support vector machine neural networks: a case study of the Cheshmeh Khush Field, South Iran
Accurate permeability estimation has always been a concern in determining flow units, assigning appropriate capillary pressure andrelative permeability curves to reservoir rock types, geological modeling, and dynamic simulation.Acoustic method can be used as analternative and effective tool for permeability determination. In this study, a four-step approach is proposed for permeability estimati...
متن کامل