Class-incremental Learning using a Sequence of Partial Implicitly Regularized Classifiers

Learning Classifiers Using Hierarchically Structured Class Taxonomies

We consider classification problems in which the class labels are organized into an abstraction hierarchy in the form of a class taxonomy. We define a structured label classification problem. We explore two approaches for learning classifiers in such a setting. We also develop a class of performance measures for evaluating the resulting classifiers. We present preliminary results that demonstra...

A New Class of Banach Sequence Spaces

Probabilistic Aggregation of Classifiers for Incremental Learning

We work with a recently proposed algorithm where an ensemble of base classifiers, combined using weighted majority voting, is used for incremental classification of data. To successfully accommodate novel information without compromising previously acquired knowledge this algorithm requires an adequate strategy to determine the voting weights. Given an instance to classify, we propose to define...

automatic generation of novel intrusion signatures using one-class classifiers and inductive learning methods (technical note)

in this paper, we propose an approach for automatic generation of novel intrusion signatures. this approach can be used in the signature-based network intrusion detection systems (nidss) and for the automation of the process of intrusion detection in these systems. in the proposed approach, first, by using several one-class classifiers, the profile of the normal network traffic is established. ...

Evolving Classifiers: Methods for Incremental Learning

The ability of a classifier to take on new information and classes by evolving the classifier without it having to be fully retrained is known as incremental learning. Incremental learning has been successfully applied to many classification problems, where the data is changing and is not all available at once. In this paper there is a comparison between Learn++, which is one of the most recent...