Add to ORKGAfter introducing the semi-supervised support vector machine (aka TSVM for "transductive SVM"), a few popular training strategies are briefly presented. Then the assumptions underlying semi-supervised learning are reviewed. Finally, two modern TSVM optimization techniques are applied to the spam filtering data sets of the workshop; it is shown that they can achieve excellent results, if the problem of the data being non-iid can be handled properly
This paper describes an e-mail spam filter based on local SVM, namely on the SVM classifier trained ...
The semi-supervised learning (SSL) problem has recently drawn large attention in the machine learnin...
Spam is commonly defined as unsolicited email messages, and the goal of spam categorization is to di...
After introducing the semi-supervised support vector machine (aka TSVM for "transductive SVM"), a fe...
We introduce a semi-supervised support vector machine (S3yM) method. Given a training set of labeled...
Due to its wide applicability, the problem of semi-supervised classification is attracting increasin...
The use of Internet is growing bit by bit and therefore huge amount of security threats faced in fro...
In this chapter we introduce basic concepts and ideas of the Support Vector Machines (SVM). In the f...
In the field of machine learning, semi-supervised learning (SSL) occupies the middle ground, between...
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 ...
Support vector machines (SVM) are a powerful tool for building good spam filtering models. However, ...
In the first part, we deal with the unlabeled data that are in good quality and follow the condition...
The paper elaborates on how text analysis influences classification—a key part of the spam-filtering...
In this paper, we develop a new algorithm for solving semi-supervised data classification problems. ...
This paper describes an e-mail spam filter based on local SVM, namely on the SVM classifier trained ...
The semi-supervised learning (SSL) problem has recently drawn large attention in the machine learnin...
Spam is commonly defined as unsolicited email messages, and the goal of spam categorization is to di...
After introducing the semi-supervised support vector machine (aka TSVM for "transductive SVM"), a fe...
We introduce a semi-supervised support vector machine (S3yM) method. Given a training set of labeled...
Due to its wide applicability, the problem of semi-supervised classification is attracting increasin...
The use of Internet is growing bit by bit and therefore huge amount of security threats faced in fro...
In this chapter we introduce basic concepts and ideas of the Support Vector Machines (SVM). In the f...
In the field of machine learning, semi-supervised learning (SSL) occupies the middle ground, between...
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 ...
Support vector machines (SVM) are a powerful tool for building good spam filtering models. However, ...
In the first part, we deal with the unlabeled data that are in good quality and follow the condition...
The paper elaborates on how text analysis influences classification—a key part of the spam-filtering...
In this paper, we develop a new algorithm for solving semi-supervised data classification problems. ...
This paper describes an e-mail spam filter based on local SVM, namely on the SVM classifier trained ...
The semi-supervised learning (SSL) problem has recently drawn large attention in the machine learnin...
Spam is commonly defined as unsolicited email messages, and the goal of spam categorization is to di...