Abrupt change detection with One-Class Time-Adaptive Support Vector Machines
نویسندگان
چکیده
منابع مشابه
Fuzzy one-class support vector machines
In one-class classification, the problem is to distinguish one class of data from the rest of the feature space. It is important in many applications where one of the classes is characterized well, while no measurements are available for the other class. Schölkopf et al. first introduced a method of adapting the support vector machine (SVM) methodology to the one-class classification problem, c...
متن کاملSpeaker Change Detection using Support Vector Machines
Speaker change detection is important for automatic segmentation of multispeaker speech data into homogeneous segments with each segment containing the data of one speaker only. Existing approaches for speaker change detection are based on the dissimilarity of the distributions of the data before and after a speaker change point. In this paper, we propose a classification based technique for sp...
متن کاملAdaptive and Efficient Image Retrieval with One-Class Support Vector Machines for Inter-Query Learning
We present an extension of previous work on improving the initial image retrieval set by exploiting both intra and inter-query learning. In most Content-Based Image Retrieval (CBIR) systems based on Relevance Feedback (RF), all prior experience is lost whenever a user generates a new query, thus inter-query information is not used. In previous work, a system was developed that learns One-class ...
متن کاملWagging for Combining Weighted One-class Support Vector Machines
Most of machine learning problems assume, that we have at our disposal objects originating from two or more classes. By learning from a representative training set a classifier is able to estimate proper decision boundaries. However, in many real-life problems obtaining objects from some of the classes is difficult, or even impossible. In such cases, we are dealing with oneclass classification,...
متن کاملUnsupervised Parameter Estimation for One-Class Support Vector Machines
Although the hyper-plane based One-Class Support Vector Machine (OCSVM) and the hyper-spherical based Support Vector Data Description (SVDD) algorithms have been shown to be very effective in detecting outliers, their performance on noisy and unlabeled training data has not been widely studied. Moreover, only a few heuristic approaches have been proposed to set the different parameters of these...
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: Expert Systems with Applications
سال: 2013
ISSN: 0957-4174
DOI: 10.1016/j.eswa.2013.06.074