منابع مشابه
Combining One-Class Classifiers
In the problem of one-class classification target objects should be distinguished from outlier objects. In this problem it is assumed that only information of the target class is available while nothing is known about the outlier class. Like standard two-class classifiers, one-class classifiers hardly ever fit the data distribution perfectly. Using only the best classifier and discarding the cl...
متن کاملUncertainty sampling methods for one-class classifiers
Selective sampling, a part of the active learning method, reduces the cost of labeling supplementary training data by asking for the labels only of the most informative, unlabeled examples. This additional information added to an initial, randomly chosen training set is expected to improve the generalization performance of a learning machine. We investigate some methods for a selection of the m...
متن کاملOne-Class LP Classifiers for Dissimilarity Representations
Problems in which abnormal or novel situations should be detected can be approached by describing the domain of the class of typical examples. These applications come from the areas of machine diagnostics, fault detection, illness identification, or, in principle, refer to any problem where little knowledge is available outside the typical class. In this paper, we explain why proximities are na...
متن کاملBinary Collaborative Filtering by One-class Classifiers
Contact Information: Marcel J. T. Reinders, Jun Wang Short Description: Collaborative filtering (CF) is any algorithm that filters information for a user based on a collection of user profiles. Since users having similar profiles may share similar interests. For a user, information can be filtered in/out regarding to his similar users' behaviors. User profiles can be either explicitly obtained ...
متن کاملCombining Dissimilarity-Based One-Class Classifiers
We address a one-class classification (OCC) problem aiming at detection of objects that come from a pre-defined target class. Since the non-target class is ill-defined, an effective set of features discriminating between the targets and non-targets is hard to obtain. Alternatively, when raw data are available, dissimilarity representations describing an object by its dissimilarities to a set of...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Transactions on Neural Networks and Learning Systems
سال: 2015
ISSN: 2162-237X,2162-2388
DOI: 10.1109/tnnls.2015.2418332