Parsimonious Support Vector Regression using Orthogonal Forward Selection with the Generalized Kernel Model
نویسندگان
چکیده
Sparse regression modeling is addressed using a generalized kernel model in which kernel regressor has its individually tuned position (center) vector and diagonal covariance matrix. An orthogonal least squares forward selection procedure is employed to append regressors one by one. After the determination of the model structure, namely the selection certain number of regressors, the model weight parameters are calculated from the Lagrange dual problem of the regression problem with the regularized linear − ε insensitive loss function. Different from the support vector regression, this stage of the procedure involves neither reproducing kernel Hilbert nor Mercer decomposition concepts and thus the difficulties associated with selecting a mapping from the input space to the feature space, needed in the support vector machine methods, can be avoided. Moreover, as the regressors used here are not restricted to be positioned at training input points and each regressor has its own diagonal covariance matrix, sparser representation can be obtained. Experimental results involving one toy example and two data sets demonstrate the effectiveness of the proposed regression modeling approach.
منابع مشابه
Parsimonious least squares support vector regression using orthogonal forward selection with the generalised kernel model
A sparse regression modelling technique is developed using a generalised kernel model in which each kernel regressor has its individually tuned position (centre) vector and diagonal covariance matrix. An orthogonal least squares forward selection procedure is employed to append the regressors one by one. After the determination of the model structure, namely the selection of an appropriate numb...
متن کاملApplying Combined Approach of Sequential Floating Forward Selection and Support Vector Machine to Predict Financial Distress of Listed Companies in Tehran Stock Exchange Market
Objective: Nowadays, financial distress prediction is one of the most important research issues in the field of risk management that has always been interesting to banks, companies, corporations, managers and investors. The main objective of this study is to develop a high performance predictive model and to compare the results with other commonly used models in financial distress prediction M...
متن کاملDevelopment of a Pharmacogenomics Model based on Support Vector Regression with Optimal Features Selection Approach to Determine the Initial Therapeutic Dose of Warfarin Anticoagulant Drug
Introduction: Using artificial intelligence tools in pharmacogenomics is one of the latest bioinformatics research fields. One of the most important drugs that determining its initial therapeutic dose is difficult is the anticoagulant warfarin. Warfarin is an oral anticoagulant that, due to its narrow therapeutic window and complex interrelationships of individual factors, the selection of its ...
متن کاملDevelopment of a Pharmacogenomics Model based on Support Vector Regression with Optimal Features Selection Approach to Determine the Initial Therapeutic Dose of Warfarin Anticoagulant Drug
Introduction: Using artificial intelligence tools in pharmacogenomics is one of the latest bioinformatics research fields. One of the most important drugs that determining its initial therapeutic dose is difficult is the anticoagulant warfarin. Warfarin is an oral anticoagulant that, due to its narrow therapeutic window and complex interrelationships of individual factors, the selection of its ...
متن کاملSparse support vector regression based on orthogonal forward selection for the generalised kernel model
This paper considers sparse regression modelling using a generalised kernel model in which each kernel regressor has its individually tuned centre vector and diagonal covariance matrix. An orthogonal least squares forward selection procedure is employed to select the regressors one by one, so as to determine the model structure. After the regressor selection, the corresponding model weight para...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2004