Murat Semerci

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We discuss approaches to incrementally construct an ensemble. The first constructs an ensemble of classifiers choosing a subset from a larger set, and the second constructs an ensemble of discriminants, where a classifier is used for some classes only. We investigate criteria including accuracy, significant improvement, diversity, correlation, and the role(More)
Graph classification is an important data mining task, and various graph kernel methods have been proposed recently for this task. These methods have proven to be effective, but they tend to have high computational overhead. In this paper, we propose an alternative approach to graph classification that is based on feature vectors constructed from different(More)
Graph classification is an important data mining task, and various graph kernel methods have been proposed recently for this task. These methods have proven to be effective, but they tend to have high computational overhead. In this paper, we propose an alternative approach to graph classification that is based on feature-vectors constructed from different(More)
Discriminative language modeling (DLM) is a feature-based approach that is used as an error-correcting step after hypothesis generation in automatic speech recognition (ASR). We formulate this both as a classification and a ranking problem and employ the perceptron, the margin infused relaxed algorithm (MIRA) and the support vector machine (SVM). To(More)
This paper investigates various approaches to data sampling and dimensionality reduction for discriminative language models (DLM). Being a feature based language modeling approach, the aim of DLM is to rerank the ASR output with discriminatively trained feature parameters. Using a Turkish morphology based feature set, we examine the use of online Principal(More)
Experimenting with large-scale real world data is crucial for the development of network protocol and investigate their performance. However, collecting such data from real networks, and especially to annotate them with ground truth proves to, if not impossible, too tedious. In such cases use of simulated data, generated for various network scenarios,(More)
SIP (Session Initiation Protocol) is currently the most popular protocol that enables session control in computer communication networks. Concomitantly with its wide deployment, SIP networks have become targets of various attacks, such as DDoS attacks. This study focuses on intelligent systems for DDoS attack monitoring based on the observation of message(More)
Discriminating the malicious users in a network is crucial in protecting the network entities and preventing any ongoing attacks. In an organized attack, a group users are supposed to behave synchronously in the same manner. In this study, we particularly focus on organized attacks where the attackers create a high volume of requests to overwhelm the server(More)
SIP (Session Initiation Protocol) is one of the most common protocols that enables session control in today's communication networks. The SIP networks are also targeted by the malicious users. This study focuses on adaptive intelligent systems that detect the changes on the network flow using anomaly detection methods. Two different change point models that(More)
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