The goal of transductive inference is to mine both labeled and unlabeled data within the same learning step and output a classification of the unlabeled examples with few errors as possible. We propose a new transductive classifier, named TRANS-SC, that works in a transductive setting and resorts to a principled probabilistic classification in multi-relational data mining to deal with structured data. The method has been evaluated on some real-world relational data collections.
TRANS-SC: a Transductive Structural Classifier
CECI, MICHELANGELO;APPICE, ANNALISA;MALERBA, Donato;
2006-01-01
Abstract
The goal of transductive inference is to mine both labeled and unlabeled data within the same learning step and output a classification of the unlabeled examples with few errors as possible. We propose a new transductive classifier, named TRANS-SC, that works in a transductive setting and resorts to a principled probabilistic classification in multi-relational data mining to deal with structured data. The method has been evaluated on some real-world relational data collections.File in questo prodotto:
Non ci sono file associati a questo prodotto.
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
