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...

We describe our participation in the Author Profiling task of the PAN 2013 competition. The task objective is to determine the age and the gender of an author of a document. We applied the Common N-Gram (CNG) classifier (Kešelj et al., 2003) to this task. The CNG classifier uses a dissimilarity measure based on the differences in the frequencies of the character n-grams that are most common in ...

The chain-rule processor is a method of constructing an optimal Bayes classifier from a bank of processors. Each processor is a feature extractor designed to separate the given class from a class-dependent reference hypothesis, thereby avoiding the curse of dimensionality. This work builds upon prior work in optimal classifier design using class-specific features. The chain-rule processor is an...

Introduction: In this paper, a method is presented to classify the breast cancer masses according to new geometric features. Methods: After obtaining digital breast mammogram images from the digital database for screening mammography (DDSM), image preprocessing was performed. Then, by using image processing methods, an algorithm was developed for automatic extracting of masses from other norma...

In the past few years, Convolutional Neural Networks (CNN) have demonstrated promising performance in various real-world cybersecurity applications, such as network and multimedia security. However, underlying fragility of CNN structures poses major security problems, making them inappropriate for use security-oriented including computer networks. Protecting these architectures from adversarial...

Many applications require the ability to identify data that is anomalous with respect to a target group of observations, in the sense of belonging to a new, previously unseen ‘attacker’ class. One possible approach to this kind of verification problem is one-class classification, learning a description of the target class concerned based solely on data from this class. However, if known non-tar...

Syntax Class, Association Object, Message Static Aspects No attributes may have the same name within a Classifier. The values of attributes have to match the declarations in the Classifier. Dynamic Aspects meaning of adding an Attribute in a Class. How an object reacts to a message.

In real-world applications, it has been often observed that class imbalance (significant differences in class prior probabilities) may produce an important deterioration of the classifier performance, in particular with patterns belonging to the less represented classes. This effect becomes especially significant on instance-based learning due to the use of some dissimilarity measure. We analyz...

ion-Based Verification of Infinite-State Reactive Modules 725 Francesco Belardinelli and Alessio Lomuscio Translation-Based Revision and Merging for Minimal Horn Reasoning 734 Gerhard Brewka, Jean-Guy Mailly and Stefan Woltran Parallel Filter-Based Feature Selection Based on Balanced Incomplete Block Designs 743 Antonio Salmerón, Anders L. Madsen, Frank Jensen, Helge Langseth, Thomas D. Nielsen...