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Some key research problems in automated theorem proving for hardware and software verification.

Matt Kaufmann, J. Strother Moore (2004)

RACSAM

This paper sketches the state of the art in the application of mechanical theorem provers to the verification of commercial computer hardware and software. While the paper focuses on the theorem proving system ACL2, developed by the two authors, it references much related work in formal methods. The paper is intended to satisfy the curiosity of readers interested in logic and artificial intelligence as to the role of mechanized theorem proving in hardware and software design today. In addition,...

Some methods of constructing kernels in statistical learning

Tomasz Górecki, Maciej Łuczak (2010)

Discussiones Mathematicae Probability and Statistics

This paper is a collection of numerous methods and results concerning a design of kernel functions. It gives a short overview of methods of building kernels in metric spaces, especially R n and S n . However we also present a new theory. Introducing kernels was motivated by searching for non-linear patterns by using linear functions in a feature space created using a non-linear feature map.

Special issue: WUPES’12

Jiřina Vejnarová, Václav Kratochvíl (2014)

Kybernetika

This special issue of the Kybernetika Journal arose from the 9th workshop on uncertainty processing, WUPES’12, held in Mariánské Lázně, Czech Republic, in September 2012. In the selection process for this special issue, we tried to capture the rich variety of the presented methodological approaches. The quality of the selected papers was judged by reviewers in accord with the usual practice of Kybernetika. After a careful selection, 7 papers were included in the special issue. There are, however,...

Spectral fuzzy classification system: a supervised approach.

Ana Del Amo, Daniel Gómez, Javier Montero (2003)

Mathware and Soft Computing

The goal of this paper is to present all algorithm for pattern recognition, leveraging on an existing fuzzy clustering algorithm developed by Del Amo et al. [3, 5], and modifying it to its supervised version, in order to apply the algorithm to different pattern recognition applications in Remote Sensing. The main goal is to recognize the object and stop the search depending on the precision of the application. The referred algorithm was the core of a classification system based on Fuzzy Sets Theory...

Statistical-learning control of multiple-delay systems with application to ATM networks

Chaouki T. Abdallah, Marco Ariola, Vladimir Koltchinskii (2001)

Kybernetika

Congestion control in the ABR class of ATM network presents interesting challenges due to the presence of multiple uncertain delays. Recently, probabilistic methods and statistical learning theory have been shown to provide approximate solutions to challenging control problems. In this paper, using some recent results by the authors, an efficient statistical algorithm is used to design a robust, fixed-structure, controller for a high-speed communication network with multiple uncertain propagation...

Stock price forecasting: Autoregressive modelling and fuzzy neural network.

Dusan Marcek (2000)

Mathware and Soft Computing

Most models for the time series of stock prices have centered on autoregresive (AR) processes. Traditionaly, fundamental Box-Jenkins analysis [3] have been the mainstream methodology used to develop time series models. Next, we briefly describe the develop a classical AR model for stock price forecasting. Then a fuzzy regression model is then introduced. Following this description, an artificial fuzzy neural network based on B-spline member ship function is presented as an alternative to the stock...

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