Add to ORKGSemiparametric models are useful tools in the case where domain knowledge exists about the function to be estimated or emphasis is put onto understandability of the model. We extend two learning algorithms- Support Vector machines and Linear Programming machines to this case and give experimental results for SV ma-chines.
A common belief is that Machine Learning Theory (MLT) is not very useful, in pratice, for performing...
We show how the SVM can be viewed as a maximum likelihood estimate of a class of probabilistic model...
Abstract. The tutorial starts with an overview of the concepts of VC dimension and structural risk m...
Semiparametric models are useful tools in the case where domain knowledge exists about the function ...
Semiparametric models are useful tools in the case where domain knowledge exists about the function ...
In this tutorial we give an overview of the basic ideas underlying Support Vector (SV) machines for ...
We briefly describe the main ideas of statistical learning theory, support vector machines (SVMs), a...
Three novel algorithms are presented; the linear programming (LP) machine for pattern classification...
We introduce a semi-supervised support vector machine (S3yM) method. Given a training set of labeled...
Support vector machines (SVMs) are a family of machine learning methods, originally introduced for t...
We briefly describe the main ideas of statistical learning theory, support vector machines (SVMs), a...
SIGLEAvailable from British Library Document Supply Centre-DSC:7769.08577(706) / BLDSC - British Lib...
After introducing the semi-supervised support vector machine (aka TSVM for "transductive SVM"), a fe...
We briefly describe the main ideas of statistical learning theory, support vector machines (SVMs), &...
Abstract: Support Vector Machines (SVMs) have become an increasingly popular tool for machine learni...
A common belief is that Machine Learning Theory (MLT) is not very useful, in pratice, for performing...
We show how the SVM can be viewed as a maximum likelihood estimate of a class of probabilistic model...
Abstract. The tutorial starts with an overview of the concepts of VC dimension and structural risk m...
Semiparametric models are useful tools in the case where domain knowledge exists about the function ...
Semiparametric models are useful tools in the case where domain knowledge exists about the function ...
In this tutorial we give an overview of the basic ideas underlying Support Vector (SV) machines for ...
We briefly describe the main ideas of statistical learning theory, support vector machines (SVMs), a...
Three novel algorithms are presented; the linear programming (LP) machine for pattern classification...
We introduce a semi-supervised support vector machine (S3yM) method. Given a training set of labeled...
Support vector machines (SVMs) are a family of machine learning methods, originally introduced for t...
We briefly describe the main ideas of statistical learning theory, support vector machines (SVMs), a...
SIGLEAvailable from British Library Document Supply Centre-DSC:7769.08577(706) / BLDSC - British Lib...
After introducing the semi-supervised support vector machine (aka TSVM for "transductive SVM"), a fe...
We briefly describe the main ideas of statistical learning theory, support vector machines (SVMs), &...
Abstract: Support Vector Machines (SVMs) have become an increasingly popular tool for machine learni...
A common belief is that Machine Learning Theory (MLT) is not very useful, in pratice, for performing...
We show how the SVM can be viewed as a maximum likelihood estimate of a class of probabilistic model...
Abstract. The tutorial starts with an overview of the concepts of VC dimension and structural risk m...