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A new efficient algorithm is presented for joint diagonalization of several matrices. The algorithm is based on the Frobenius-norm formulation of the joint diagonalization problem, and addresses di-agonalization with a general, non-orthogonal transformation. The iterative scheme of the algorithm is based on a multiplicative update which ensures the(More)
Learning linear combinations of multiple kernels is an appealing strategy when the right choice of features is unknown. Previous approaches to multiple kernel learning (MKL) promote sparse kernel combinations to support interpretability. Unfortunately, 1-norm MKL is hardly observed to outperform trivial baselines in practical applications. To allow for(More)
Main idea: search for anomalies in the data without training on the clean data. Advantages: no need for training, no need for extensive amount of clean data. Reproduce the state-of-the-art results on the KDD Cup (DARPA '98) dataset (with the main focus on one-class SVM). Investigate the methods from the machine learning point of view. Investigate the(More)
Malicious software in form of Internet worms, computer viruses, and Trojan horses poses a major threat to the security of networked systems. The diversity and amount of its variants severely undermine the e«ectiveness of classical signature-based detection. Yet variants of malware families share typical behavioral patterns reflecting its origin and purpose.(More)
In this thesis we consider statistical learning problems and machines. A statistical learning machine tries to infer rules from a given set of examples such that it is able to make correct predictions on unseen examples. These predictions can for example be a classification or a regression. We consider the class of kernel based learning techniques. The main(More)
In security-sensitive applications, the success of machine learning depends on a thorough vetting of their resistance to adversarial data. In one pertinent, well-motivated attack scenario, an adversary may attempt to evade a deployed system at test time by carefully manipulating attack samples. In this work, we present a simple but effective gradient-based(More)
Learning-based classifiers are increasingly used for detection of various forms of malicious data. However, if they are deployed online, an attacker may attempt to evade them by manipulating the data. Examples of such attacks have been previously studied under the assumption that an attacker has full knowledge about the deployed classifier. In practice,(More)
Malicious PDF files remain a real threat, in practice, to masses of computer users, even after several high-profile security incidents. In spite of a series of a security patches issued by Adobe and other vendors, many users still have vulnerable client software installed on their computers. The expressiveness of the PDF format, furthermore, enables(More)
Incremental Support Vector Machines (SVM) are instrumental in practical applications of online learning. This work focuses on the design and analysis of efficient incremental SVM learning, with the aim of providing a fast, numerically stable and robust implementation. A detailed analysis of convergence and of algorithmic complexity of incremental SVM(More)