A survey of recent trends in one class classification <s> Tax and Duin <s> Humans appear to be able to learn new concepts without needing to be programmed explicitly in any conventional sense. In this paper we regard learning as the phenomenon of knowledge acquisition in the absence of explicit programming. We give a precise methodology for studying this phenomenon from a computational viewpoint. It consists of choosing an appropriate information gathering mechanism, the learning protocol, and exploring the class of concepts that can be learnt using it in a reasonable (polynomial) number of steps. We find that inherent algorithmic complexity appears to set serious limits to the range of concepts that can be so learnt. The methodology and results suggest concrete principles for designing realistic learning systems. <s> BIB001 </s> A survey of recent trends in one class classification <s> Tax and Duin <s> We consider the problem of using a large unlabeled sample to boost performance of a learning algorit,hrn when only a small set of labeled examples is available. In particular, we consider a problem setting motivated by the task of learning to classify web pages, in which the description of each example can be partitioned into two distinct views. For example, the description of a web page can be partitioned into the words occurring on that page, and the words occurring in hyperlinks t,hat point to that page. We assume that either view of the example would be sufficient for learning if we had enough labeled data, but our goal is to use both views together to allow inexpensive unlabeled data to augment, a much smaller set of labeled examples. Specifically, the presence of two distinct views of each example suggests strategies in which two learning algorithms are trained separately on each view, and then each algorithm’s predictions on new unlabeled examples are used to enlarge the training set of the other. Our goal in this paper is to provide a PAC-style analysis for this setting, and, more broadly, a PAC-style framework for the general problem of learning from both labeled and unlabeled data. We also provide empirical results on real web-page data indicating that this use of unlabeled examples can lead to significant improvement of hypotheses in practice. *This research was supported in part by the DARPA HPKB program under contract F30602-97-1-0215 and by NSF National Young investigator grant CCR-9357793. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. TO copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. COLT 98 Madison WI USA Copyright ACM 1998 l-58113-057--0/98/ 7...%5.00 92 Tom Mitchell School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213-3891 mitchell+@cs.cmu.edu <s> BIB002 </s> A survey of recent trends in one class classification <s> Tax and Duin <s> Single-class classifier learning is the problem of learning a classifier from a set of training examples in which only examples of the target class are present. Most existing approaches to this problem are based on density estimation and hence suffer from the usual problems associated with estimating probability densities in high dimensional spaces. This paper describes how feedforward neural networks can be used to learn a classifier from a dataset consisting of (labeled) examples of the target class (positive examples) together with a corpus of unlabeled (positive and negative) examples. An application of the technique to the prediction of mineral deposit location is provided, and empirical results are presented. <s> BIB003
For many learning tasks, labeled examples are rare, whereas numerous unlabeled examples are easily available. The problem of learning with the help of unlabeled data given a small set of labeled examples is studied by Blum and Mitchell BIB002 by using the concept of co-training for text classification. The cotraining setting can be applied when a data set has natural separation of its features. Co-training algorithms incrementally build basic classifiers over each of these feature sets. They show that under the assumptions that each set of features is sufficient for classification, and the feature sets of each instance are conditionally independent, given the class, PAC (Probably Approximately Correct) learning BIB001 guarantees on learning from labeled and unlabeled data. Muggleton  presents a theoretical study in the Bayesian framework where the distribution of functions and examples are assumed to be known. Skabar BIB003 describes the use of feed-forward neural network to learn a classifier from a data set consisting of labeled positive examples along with a corpus of unlabeled examples containing positive and negative samples.