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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)
has been working as a research scientist at the intelligent data analysis (IDA) group of the Fraunhofer FIRST. His main research interests include design and analysis of machine learning algorithms and applications of machine learning in computer security. Christin Schäfer received the diploma in statistics in 2001 from the University of Dortmund, Germany.(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)
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)
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)
Application and development of specialized machine learning techniques is gaining increasing attention in the intrusion detection community. A variety of learning techniques proposed for different intrusion detection problems can be roughly classified into two broad categories: supervised (classification) and unsupervised (anomaly detection and clustering).(More)
Approaches to multiple kernel learning (MKL) employ ℓ 1-norm constraints on the mixing coefficients to promote sparse kernel combinations. When features encode orthogonal characterizations of a problem, sparseness may lead to discarding useful information and may thus result in poor generalization performance. We study non-sparse multiple kernel learning by(More)
We present a new approach to approximate joint diagonalization of a set of matrices. The main advantages of our method are computational efficiency and generality. We develop an iterative procedure, called LSDIAG, which is based on multiplicative updates and on linear least-squares optimization. The efficiency of our algorithm is achieved by the first-order(More)
Despite the recent security improvements in Adobe's PDF viewer, its underlying code base remains vulnerable to novel exploits. A steady flow of rapidly evolving PDF malware observed in the wild substantiates the need for novel protection instruments beyond the classical signature-based scanners. In this contribution we present a technique for detection of(More)