This paper presents our approach to the 2013 Native Language Identification shared task, which is based on machine learning methods that work at the character level. More precisely, we used several string kernels and a kernel based on Local Rank Distance (LRD). Actually, our best system was a kernel combination of string kernel and LRD. While string kernels have been used before in text analysi...

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One novel composite kernel based support vector machine (SVM), which is called DOCKSVM (Data Oriented Composite Kernel based Support Vector Machine) is proposed in the paper. SVM have been proved good potential in various studies, and tried to application for pattern classification problems such as text categorization, image classification, objects detection etc. Recently, more and more researc...

For (possibly unstable) ODE systems with actuator delay, predictor-based infinite-dimensional feedback can compensate for actuator delay of arbitrary length and achieve stabilization.We extend this concept to another class of PDE-ODE cascades, where the infinite-dimensional part of the plant is of diffusive, rather than convective type. We derive predictor-like feedback laws and observers, with...

We present a machine learning approach for the Arabic Dialect Identification (ADI) and the German Dialect Identification (GDI) Closed Shared Tasks of the DSL 2017 Challenge. The proposed approach combines several kernels using multiple kernel learning. While most of our kernels are based on character p-grams (also known as n-grams) extracted from speech transcripts, we also use a kernel based o...

Modern techniques for data analysis and machine learning are so called kernel methods. The most famous and successful one is represented by the support vector machine (SVM) for classification or regression tasks. Further examples are kernel principal component analysis for feature extraction or other linear classifiers like the kernel perceptron. The fundamental ingredient in these methods is t...

Ecological interaction, including competition for resources, often causes frequency-dependent disruptive selection, which, when accompanied by reproductive isolation, may act as driving forces of adaptive speciation. While adaptive dynamics models have added new perspectives to our understanding of the ecological dimensions of speciation processes, it remains an open question how best to incorp...

A key challenge in applying kernel-based methods for discriminative learning is to identify a suitable kernel given a problem domain. Many methods instead transform the input data into a set of vectors in a feature space and classify the transformed data using a generic kernel. However, finding an effective transformation scheme for sequence (e.g. time series) data is a difficult task. In this ...

Positive definite kernels play an increasingly prominent role in many applications such as scattered data fitting, numerical solution of PDEs, computer experiments, machine learning, rapid prototyping and computer graphics. We discuss some of the historical and current developments of the theory and applications of positive definite kernels – always with an eye toward the mathematics of Götting...