One-Class Classification Based on Extreme Learning and Geometric Class Information
نویسندگان
چکیده
منابع مشابه
One-Class Classification with Extreme Learning Machine
One-class classification problemhas been investigated thoroughly for past decades. Among one of themost effective neural network approaches for one-class classification, autoencoder has been successfully applied for many applications. However, this classifier relies on traditional learning algorithms such as backpropagation to train the network, which is quite time-consuming. To tackle the slow...
متن کاملDetection of Fake Accounts in Social Networks Based on One Class Classification
Detection of fake accounts on social networks is a challenging process. The previous methods in identification of fake accounts have not considered the strength of the users’ communications, hence reducing their efficiency. In this work, we are going to present a detection method based on the users’ similarities considering the network communications of the users. In the first step, similarity ...
متن کاملExtreme Multi Class Classification
We consider the multi class classification problem under the setting where the number of labels is very large and hence it is very desirable to efficiently achieve train and test running times which are logarithmic in the label complexity. Additionally the labels are feature dependent in our setting. We propose a reduction of this problem to a set of binary regression problems organized in a tr...
متن کاملLearning Deep Features for One-Class Classification
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 ...
متن کاملOne-class classifier based on extreme value statistics
Interest in One-Class Classification methods has soared in recent years due to its wide applicability in many practical problems where classification in the absence of counterexamples is needed. In this paper, a new one class classification rule based on order statistics is presented. It only relies on the embedding of the classification problem into a metric space, so it is suitable for Euclid...
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: Neural Processing Letters
سال: 2016
ISSN: 1370-4621,1573-773X
DOI: 10.1007/s11063-016-9541-y