نتایج جستجو برای: one class classification
تعداد نتایج: 2663984 فیلتر نتایج به سال:
We introduce the OneClassMaxMinOver (OMMO) algorithm for the problem of one-class support vector classification. The algorithm is extremely simple and therefore a convenient choice for practitioners. We prove that in the hard-margin case the algorithm converges with O(1/ √ t) to the maximum margin solution of the support vector approach for one-class classification introduced by Schölkopf et al...
We implemented versions of the SVM appropriate for one-class classification in the context of information retrieval. The experiments were conducted on the standard Reuters data set. For the SVM implementation we used both a version of Schölkopf et al. and a somewhat different version of one-class SVM based on identifying “outlier” data as representative of the second-class. We report on experim...
In this paper we outline a PhD research plan. This research contributes to the field of one-class incremental learning and classification in case of non-stationary environments. The goal of this PhD is to define a new classification framework able to deal with very small learning dataset at the beginning of the process and with abilities to adjust itself according to the variability of the inco...
In one-class classification one tries to describe a class of target data and to distinguish it from all other possible outlier objects. Obvious applications are areas where outliers are very diverse or very difficult or expensive to measure, such as in machine diagnostics or in medical applications. In order to have a good distinction between the target objects and the outliers, good representa...
We propose a deep learning-based solution for the problem of feature learning in one-class classification. The proposed method operates on top of a Convolutional Neural Network (CNN) of choice and produces descriptive features while maintaining a low intra-class variance in the feature space for the given class. For this purpose two loss functions, compactness loss and descriptiveness loss are ...
Detecting instances of unknown categories is an important task for a multitude of problems such as object recognition, event detection, and defect localization. This paper investigates the use of Gaussian process (GP) priors for this area of research. Focusing on the task of one-class classification for visual object recognition, we analyze different measures derived from GP regression and appr...
Feature reduction is often an essential part of solving a classification task. One common approach for doing this, is Principal Component Analysis. There the low variance directions in the data are removed and the high variance directions are retained. It is hoped that these high variance directions contain information about the class differences. For one-class classification or novelty detecti...
In this work, a new one-class classification ensemble strategy called approximate polytope ensemble is presented. The main contribution of the paper is threefold. First, the geometrical concept of convex hull is used to define the boundary of the target class defining the problem. Expansions and contractions of this geometrical structure are introduced in order to avoid over-fitting. Second, th...
09:00 – 10:30 Workshop on Mining Complex and Stream Data (MCSD 2012) Machine-generated Data Analytics: Challenges and Opportunities Graham Toppin (invited talk) SONCA. Scalable Semantic Processing of Rapidly Growing Document Stores Marek Grzegorowski, Przemysław Wiktor Pardel, Sebastian Stawicki, Krzysztof Stencel Soft competitive learning for large data sets Frank-Michael Schleif, Xibin Zhu, B...
One-class classification has important applications such as outlier and novelty detection. It is commonly tackled using either density estimation techniques or by adapting a standard classification algorithm to the problem of carving out a decision boundary that describes the location of the target data. In this paper we present a simple method for one-class classification that combines the app...
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