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Multiobjective Evolutionary Algorithms: Analyzing the State-of-the-Art

by David A. Van Veldhuizen, Gary B. Lamont , 2000
"... Solving optimization problems with multiple (often conflicting) objectives is, generally, a very difficult goal. Evolutionary algorithms (EAs) were initially extended and applied during the mid-eighties in an attempt to stochastically solve problems of this generic class. During the past decade, ..."
Abstract - Cited by 440 (7 self) - Add to MetaCart
, a variety of multiobjective EA (MOEA) techniques have been proposed and applied to many scientific and engineering applications. Our discussion's intent is to rigorously define multiobjective optimization problems and certain related concepts, present an MOEA classification scheme

Dynamic Bayesian Networks: Representation, Inference and Learning

by Kevin Patrick Murphy , 2002
"... Modelling sequential data is important in many areas of science and engineering. Hidden Markov models (HMMs) and Kalman filter models (KFMs) are popular for this because they are simple and flexible. For example, HMMs have been used for speech recognition and bio-sequence analysis, and KFMs have bee ..."
Abstract - Cited by 770 (3 self) - Add to MetaCart
Modelling sequential data is important in many areas of science and engineering. Hidden Markov models (HMMs) and Kalman filter models (KFMs) are popular for this because they are simple and flexible. For example, HMMs have been used for speech recognition and bio-sequence analysis, and KFMs have

Incorporating non-local information into information extraction systems by Gibbs sampling

by Jenny Rose Finkel, Trond Grenager, Christopher Manning - IN ACL , 2005
"... Most current statistical natural language processing models use only local features so as to permit dynamic programming in inference, but this makes them unable to fully account for the long distance structure that is prevalent in language use. We show how to solve this dilemma with Gibbs sampling, ..."
Abstract - Cited by 730 (25 self) - Add to MetaCart
use this technique to augment an existing CRF-based information extraction system with long-distance dependency models, enforcing label consistency and extraction template consistency constraints. This technique results in an error reduction of up to 9 % over state-of-the-art systems on two

Diagnosing multiple faults.

by Johan De Kleer , Brian C Williams - Artificial Intelligence, , 1987
"... Abstract Diagnostic tasks require determining the differences between a model of an artifact and the artifact itself. The differences between the manifested behavior of the artifact and the predicted behavior of the model guide the search for the differences between the artifact and its model. The ..."
Abstract - Cited by 808 (62 self) - Add to MetaCart
. The diagnostic procedure presented in this paper is model-based, inferring the behavior of the composite device from knowledge of the structure and function of the individual components comprising the device. The system (GDE -General Diagnostic Engine) has been implemented and tested on many examples

A Practical Tool for MassCustomising Configurable Products

by Juha Tiihonen, Timo Soininen, Ilkka Niemelä, Reijo Sulonen - In Proceedings of International Conference on Engineering Design (ICED 03 , 2003
"... Configurable products are an important way to achieve mass-customisation to satisfy individ-ual customer requirements. We describe a novel configurator prototype that supports tailoring, i.e. configuring, such a product. It contains a semi-visual modelling tool based on a high-level object- and prod ..."
Abstract - Cited by 12 (6 self) - Add to MetaCart
- and product structure-oriented modelling language with a clear formal semantics. The main functionality is delivered by a configuration support tool aimed at e-commerce. This tool provides intelligent support for configuring a product by applying a state-of-the-art inference engine for the form of logic

Reasoning with Large Scale

by unknown authors
"... Abstract—The MapReduce framework has proved to be very efficient for data-intensive tasks. Earlier work has successfully applied MapReduce for large scale RDFS/OWL reasoning. In this paper, we move a step forward by considering scalable reasoning on semantic data under fuzzy pD * semantics (i.e., an ..."
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system is implemented for the evaluation purpose. The experimental results show that the running time of our system is comparable with that of WebPIE, the state-of-the-art inference engine for scalable reasoning in pD * semantics.

Large Scale Fuzzy pD ∗ Reasoning Using

by Chang Liu, Guilin Qi, Haofen Wang, Yong Yu - MapReduce’, in International Semantic Web Conference , 2011
"... Abstract. The MapReduce framework has proved to be very efficient for data-intensive tasks. Earlier work has tried to use MapReduce for large scale reasoning for pD ∗ semantics and has shown promising results. In this paper, we move a step forward to consider scalable reasoning on top of semantic da ..."
Abstract - Cited by 2 (2 self) - Add to MetaCart
. The experimental results show that the running time of our system is comparable with that of WebPIE, the state-of-the-art inference engine for scalable reasoning in pD ∗ semantics. 1

Hierarchical Modelling and Analysis for Spatial Data. Chapman and Hall/CRC,

by S Banerjee , B P Carlin , A E Gelfand , Chapman , / Hall , New Crc , N York; Cressie , P J Diggle , P J Ribeiro Jr , B D Ripley , 2004
"... Abstract Often, there are two streams in statistical research -one developed by practitioners and other by main stream statisticians. Development of geostatistics is a very good example where pioneering work under realistic assumptions came from mining engineers whereas it is only now that statisti ..."
Abstract - Cited by 442 (45 self) - Add to MetaCart
(The talk will have several illustrated examples). The conclusion is that the ML method is feasible for geostatistics; it can be implemented efficiently and provides a powerful tool for geostatistical inference. It provides a complete approach to variogram inference offering methodology for model

Reveal, A General Reverse Engineering Algorithm For Inference Of Genetic Network Architectures

by Shoudan Liang, Stefanie Fuhrman, Roland Somogyi , 1998
"... Given the immanent gene expression mapping covering whole genomes during development, health and disease, we seek computational methods to maximize functional inference from such large data sets. Is it possible, in principle, to completely infer a complex regulatory network architecture from input/o ..."
Abstract - Cited by 344 (5 self) - Add to MetaCart
to infer the sets of input elements controlling each element or gene in the network. This process is unequivocal and exact for complete state transition tables. We implemented this REVerse Engineering ALgorithm (REVEAL) in a C program, and found the problem to be tractable within the conditions tested so

Neuro Fuzzy Systems: State-of-the-art Modeling Techniques

by Ajith Abraham - AND ARTIFICIAL INTELLIGENCE, SPRINGER-VERLAG GERMANY, JOSE MIRA AND ALBERTO PRIETO (EDS , 2001
"... Fusion of Artificial Neural Networks (ANN) and Fuzzy Inference Systems (FIS) have attracted the growing interest of researchers in various scientific and engineering areas due to the growing need of adaptive intelligent systems to solve the real world problems. ANN learns from scratch by adjustin ..."
Abstract - Cited by 62 (36 self) - Add to MetaCart
Fusion of Artificial Neural Networks (ANN) and Fuzzy Inference Systems (FIS) have attracted the growing interest of researchers in various scientific and engineering areas due to the growing need of adaptive intelligent systems to solve the real world problems. ANN learns from scratch
Results 1 - 10 of 7,590