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This work presents LIGHT, a feature constraint language for deduction-based bottom-up parsing with typed-unification grammars. We overview both its formal definition, as a logic language operating bottom-up inferences over OSF-terms, and its implementation — an elegant combination of a virtual machine for head-corner parsing and an extended abstract machine for feature structure unification.
We defined FCGlight, a refined version of the Fluid Construction Grammar (FCG), which is a formalism for studying the evolution of the natural language. We picked a core subset of FCG, and expressed it in the semantic framework of the Order-Sorted Features (OSF) logic. This allows for efficient processing, and also gives FCG a solid formal background for further analysis and improvement. Inspired...
The aim of this work in progress is twofold: first to find out a significant, as large as possible subset of the fluid construction grammar (FCG) formalism that can be supported by an efficient implementation, and second to check whether the LIGHT platform which until now was used for running large scale unification grammars can be (and if so, how it will be) extended so as to support evolving grammars...
In this paper we explore the capabilities of a framework that can use different machine learning algorithms to successfully detect malware files, aiming to minimize the number of false positives. We report the results obtained in our framework, working firstly with cascades of one-sided perceptron and kernelized one-sides perceptrons and secondly with cascade of one-sided support vector machines.
We propose a versatile framework in which one can employ different machine learning algorithms to successfully distinguish between malware files and clean files, while aiming to minimise the number of false positives. In this paper we present the ideas behind our framework by working firstly with cascade one-sided perceptrons and secondly with cascade kernelized one-sided perceptrons. After having...
We designed a new SVM for microRNA identification, whose novelty consist in the fact that many of its features incorporate the base-pairing probabilities provided by McCaskill's algorithm. Comparisons with other SVMs for microRNA identification prove that our SVM obtains competitive results. One of the advantages of our approach is that it makes no use of so-called normalised features which are based...
In this paper we discuss the idea of combining old-fashioned computer Go with Monte Carlo Go. We introduce an analyze-after approach to random simulations. We also briefly present the other features of our present Monte Carlo implementation with upper confidence trees. We then explain our approach to adding this implementation as a module in the GNU Go 3.6 engine, and finally show some preliminary...
This paper proposes a technique for learning kernel functions that can be used in non-linear SVM classification. The technique uses genetic programming to evolve kernel functions as additive or multiplicative combinations of linear, polynomial and RBF kernels, while a procedure inspired from InfoBoost helps the evolved kernels concentrate on the most difficult objects to classify. The kernels obtained...
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