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	<title><![CDATA[neural network Resources | BNET]]></title>
	<link><![CDATA[http://resources.bnet.com/topic/neural+network.html]]></link>
	<description><![CDATA[White papers, case studies, business articles, and blog posts relating to neural network]]></description>
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	<language>en-us</language>
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		<title><![CDATA[Quantitative Analysts Have Fastest Linear Programming Optimization, Classification Neural Networks, and Visualization Tools From Visual Numerics(R) IMSL(TM) C# and JMSL(TM) Numerical Libraries for Portfolio Modeling]]></title>
		<link><![CDATA[http://findarticles.com/p/articles/mi_pwwi/is_200606/ai_n16473703]]></link>
		<description><![CDATA[Visual Numerics , Inc., celebrating 35 years of producing leading  numerical analysis  and  visualization software , today announced the availability of the IMSL C# Numerical Library version 4.0 and JMSL Numerical Library for JavaTM Applications version 4.0. These libraries now include the world's fastest, most robust high...]]></description>
		<s:doctype><![CDATA[Research articles]]></s:doctype>
		<pubDate>Wed, 14 Jun 2006 00:00:00 -0700</pubDate>
		<category domain="http://resources.bnet.com/topic/c%2523.html"><![CDATA[C#]]></category>
		<category domain="http://resources.bnet.com/topic/modeling.html"><![CDATA[modeling]]></category>
		<category domain="http://resources.bnet.com/topic/network.html"><![CDATA[network]]></category>
		<category domain="http://resources.bnet.com/topic/neural+network.html"><![CDATA[neural network]]></category>
		<category domain="http://resources.bnet.com/topic/programming.html"><![CDATA[programming]]></category>
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		<category domain="http://resources.bnet.com/topic/visual+numerics.html"><![CDATA[Visual Numerics]]></category>
		<category domain="http://resources.bnet.com/topic/.html"><![CDATA[]]></category>
	</item>
	<item>
		<title><![CDATA[Visual Numerics(R) Announces the IMSL(TM) C Numerical Library Version 6.0 With World-Class Linear Programming Optimization Technology and Advanced Forecasting Package, Including Neural Networks and Auto_ARIMA]]></title>
		<link><![CDATA[http://findarticles.com/p/articles/mi_pwwi/is_200602/ai_n16079862]]></link>
		<description><![CDATA[Visual Numerics, Inc., celebrating 35 years of producing leading numerical analysis and visualization software, today extended its leadership in numerical analysis and optimization with a new version of its flagship IMSL&#153; C Numerical Library, used worldwide for data analysis in finance, technical, and business environments. IMSL&#153; C Numerical Library 6.0...]]></description>
		<s:doctype><![CDATA[Research articles]]></s:doctype>
		<pubDate>Tue, 21 Feb 2006 00:00:00 -0800</pubDate>
		<category domain="http://resources.bnet.com/topic/c.html"><![CDATA[C]]></category>
		<category domain="http://resources.bnet.com/topic/network.html"><![CDATA[network]]></category>
		<category domain="http://resources.bnet.com/topic/neural+network.html"><![CDATA[neural network]]></category>
		<category domain="http://resources.bnet.com/topic/programming.html"><![CDATA[programming]]></category>
		<category domain="http://resources.bnet.com/topic/visual+numerics.html"><![CDATA[Visual Numerics]]></category>
		<category domain="http://resources.bnet.com/topic/.html"><![CDATA[]]></category>
	</item>
	<item>
		<title><![CDATA[Risk Assessment of Drilling and Completion Operations in Petroleum Wells Using a Monte Carlo and a Neural Network Approach]]></title>
		<link><![CDATA[http://jobfunctions.bnet.com/abstract.aspx?docid=237274]]></link>
		<description><![CDATA[Risk analysis and management of petroleum exploration ventures is growing worldwide and many international petroleum companies have improved their exploration performance by using principles of risk analysis in combination with new technologies This paper intends to show how two different methodologies, a Monte Carlo simulation method and a connectionist approach...]]></description>
		<s:doctype><![CDATA[White papers]]></s:doctype>
		<pubDate>Tue, 08 Nov 2005 00:00:00 -0800</pubDate>
		<category domain="http://resources.bnet.com/topic/risk+assessment.html"><![CDATA[Risk Assessment]]></category>
		<category domain="http://resources.bnet.com/topic/network.html"><![CDATA[Network]]></category>
		<category domain="http://resources.bnet.com/topic/petroleum.html"><![CDATA[Petroleum]]></category>
		<category domain="http://resources.bnet.com/topic/neural+network.html"><![CDATA[Neural Network]]></category>
		<category domain="http://resources.bnet.com/topic/risk+analysis.html"><![CDATA[Risk Analysis]]></category>
		<category domain="http://resources.bnet.com/topic/telecom+%2526+utilities.html"><![CDATA[Telecom & Utilities]]></category>
	</item>
	<item>
		<title><![CDATA[Modeling Brand Choice Using Boosted and Stacked Neural Networks]]></title>
		<link><![CDATA[http://jobfunctions.bnet.com/abstract.aspx?docid=135685]]></link>
		<description><![CDATA[The brand choice problem in marketing has recently been addressed with methods from computational intelligence such as neural networks. Another class of methods from computational intelligence, the so-called ensemble methods such as boosting and stacking has never been applied to the brand choice problem. Ensemble methods generate a number of...]]></description>
		<s:doctype><![CDATA[White papers]]></s:doctype>
		<pubDate>Thu, 10 Mar 2005 00:00:00 -0800</pubDate>
		<category domain="http://resources.bnet.com/topic/method.html"><![CDATA[Method]]></category>
		<category domain="http://resources.bnet.com/topic/brand.html"><![CDATA[Brand]]></category>
		<category domain="http://resources.bnet.com/topic/erasmus+university+rotterdam.html"><![CDATA[Erasmus University Rotterdam]]></category>
		<category domain="http://resources.bnet.com/topic/network.html"><![CDATA[Network]]></category>
		<category domain="http://resources.bnet.com/topic/neural+network.html"><![CDATA[Neural Network]]></category>
		<category domain="http://resources.bnet.com/topic/modeling.html"><![CDATA[Modeling]]></category>
		<category domain="http://resources.bnet.com/topic/branding.html"><![CDATA[Branding]]></category>
		<category domain="http://resources.bnet.com/topic/marketing.html"><![CDATA[Marketing]]></category>
	</item>
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		<title><![CDATA[Comparison of Artificial Neural Network and Logistic Regression Models for Prediction of Mortality in Head Trauma Based on Initial Clinical Data]]></title>
		<link><![CDATA[http://jobfunctions.bnet.com/abstract.aspx?docid=159027]]></link>
		<description><![CDATA[The outcome prediction models using Artificial Neural Network ANN and multivariable logistic regression analysis have been developed in many areas of health care research. Both these methods have advantages and disadvantages. This paper talks about a study which compares the performance of artificial neural network and multivariable logistic regression models,...]]></description>
		<s:doctype><![CDATA[White papers]]></s:doctype>
		<pubDate>Tue, 15 Feb 2005 00:00:00 -0800</pubDate>
		<category domain="http://resources.bnet.com/topic/network.html"><![CDATA[Network]]></category>
		<category domain="http://resources.bnet.com/topic/biomed+central.html"><![CDATA[BioMed Central]]></category>
		<category domain="http://resources.bnet.com/topic/neural+network.html"><![CDATA[Neural Network]]></category>
		<category domain="http://resources.bnet.com/topic/model.html"><![CDATA[Model]]></category>
		<category domain="http://resources.bnet.com/topic/trauma.html"><![CDATA[Trauma]]></category>
		<category domain="http://resources.bnet.com/topic/performance+management.html"><![CDATA[Performance Management]]></category>
		<category domain="http://resources.bnet.com/topic/human+resources.html"><![CDATA[Human Resources]]></category>
		<category domain="http://resources.bnet.com/topic/workforce+management.html"><![CDATA[Workforce Management]]></category>
	</item>
	<item>
		<title><![CDATA[Neural Network Analysis in Pharmacogenetics of Mood Disorders]]></title>
		<link><![CDATA[http://jobfunctions.bnet.com/abstract.aspx?docid=121149]]></link>
		<description><![CDATA[This white paper study deals with testing a neural network strategy for a combined analysis of two gene polymorphisms. A Multi Layer Perceptron model showed the best performance and was therefore selected over the other networks. The polymorphism in the transcriptional control region upstream of the 5HTT coding sequence SERTPR...]]></description>
		<s:doctype><![CDATA[White papers]]></s:doctype>
		<pubDate>Thu, 09 Dec 2004 00:00:00 -0800</pubDate>
		<category domain="http://resources.bnet.com/topic/biomed+central.html"><![CDATA[BioMed Central]]></category>
		<category domain="http://resources.bnet.com/topic/polymorphism.html"><![CDATA[Polymorphism]]></category>
		<category domain="http://resources.bnet.com/topic/neural+network.html"><![CDATA[Neural Network]]></category>
	</item>
	<item>
		<title><![CDATA[Analysis of Supply Chains Using System Dynamics, Neural Nets, and Eigenvalues]]></title>
		<link><![CDATA[http://jobfunctions.bnet.com/abstract.aspx?docid=164679]]></link>
		<description><![CDATA[Supply chain management is a critically significant strategy that enterprises depend on in meeting the challenges of today's highly competitive and dynamic business environments. An important aspect of supply chain management is how enterprises can detect the supply chain behavioral changes due to endogenous and/or exogenous influences and to predict...]]></description>
		<s:doctype><![CDATA[White papers]]></s:doctype>
		<pubDate>Thu, 11 Nov 2004 00:00:00 -0800</pubDate>
		<category domain="http://resources.bnet.com/topic/supply+chain.html"><![CDATA[Supply Chain]]></category>
		<category domain="http://resources.bnet.com/topic/neural+network.html"><![CDATA[Neural Network]]></category>
		<category domain="http://resources.bnet.com/topic/analysis.html"><![CDATA[Analysis]]></category>
		<category domain="http://resources.bnet.com/topic/supply+chain+management+%2528scm%2529.html"><![CDATA[Supply Chain Management (SCM)]]></category>
		<category domain="http://resources.bnet.com/topic/enterprise+software.html"><![CDATA[Enterprise Software]]></category>
		<category domain="http://resources.bnet.com/topic/software.html"><![CDATA[Software]]></category>
	</item>
	<item>
		<title><![CDATA[Selecting Cotton Bales by Spinning Consistency Index and Micronaire Using Artificial Neural Networks]]></title>
		<link><![CDATA[http://jobfunctions.bnet.com/abstract.aspx?docid=121485]]></link>
		<description><![CDATA[This paper presents a method of selecting cotton bales to meet the specified ring yarn properties using artificial neural networks. Five yarn properties and yarn count were used as inputs, whereas the Spinning Consistency Index SCI and micronaire were the outputs to the neural network models. Bales were selected according...]]></description>
		<s:doctype><![CDATA[White papers]]></s:doctype>
		<pubDate>Mon, 01 Mar 2004 00:00:00 -0800</pubDate>
		<category domain="http://resources.bnet.com/topic/network.html"><![CDATA[Network]]></category>
		<category domain="http://resources.bnet.com/topic/neural+network.html"><![CDATA[Neural Network]]></category>
		<category domain="http://resources.bnet.com/topic/property.html"><![CDATA[Property]]></category>
		<category domain="http://resources.bnet.com/topic/yarn+spinning+technology.html"><![CDATA[Yarn Spinning Technology]]></category>
	</item>
	<item>
		<title><![CDATA[Neural Nets To Identify Proteins.(Agilent Technologies Inc.)(Pacific Northwest Laboratories)(Battelle Memorial Institute)]]></title>
		<link><![CDATA[http://findarticles.com/p/articles/mi_hb5834/is_200308/ai_n23801901]]></link>
		<description><![CDATA[How long does it take for a given peptide to elute from a liquid  chromatograph?  Pacific Northwest Laboratories PNNL (P.O. Box 999,  Richland, WA 99352, Tel: 1-888/375-7665, Website: pnl.gov) has developed  an artificial neural network to predict tha  How long does it take for a...]]></description>
		<s:doctype><![CDATA[Research articles]]></s:doctype>
		<pubDate>Fri, 01 Aug 2003 00:00:00 -0700</pubDate>
		<category domain="http://resources.bnet.com/topic/agilent+technologies+inc..html"><![CDATA[Agilent Technologies Inc.]]></category>
		<category domain="http://resources.bnet.com/topic/battelle+memorial+institute.html"><![CDATA[Battelle Memorial Institute]]></category>
		<category domain="http://resources.bnet.com/topic/neural+network.html"><![CDATA[neural network]]></category>
		<category domain="http://resources.bnet.com/topic/.html"><![CDATA[]]></category>
		<category domain="http://rss.financialcontent.com/stocksymbol">A</category>
		<category domain="tickers">A</category>
	</item>
	<item>
		<title><![CDATA[Unleashing the Power of Artificial Intelligence for Microsoft Excel Forecasting and Estimation]]></title>
		<link><![CDATA[http://jobfunctions.bnet.com/abstract.aspx?docid=168196]]></link>
		<description><![CDATA[NeuroXL Predictor is a powerful, easy-to-use and affordable solution for advanced estimation and forecasting. By harnessing the latest advances in artificial intelligence and neural network technology, it delivers accurate and fast predictions for your business, financial, or sports forecasting tasks. Designed as an add-on to Microsoft Excel, it is easy...]]></description>
		<s:doctype><![CDATA[White papers]]></s:doctype>
		<pubDate>Tue, 03 Jun 2003 00:00:00 -0700</pubDate>
		<category domain="http://resources.bnet.com/topic/artificial+intelligence.html"><![CDATA[Artificial Intelligence]]></category>
		<category domain="http://resources.bnet.com/topic/neural+network.html"><![CDATA[Neural Network]]></category>
		<category domain="http://resources.bnet.com/topic/microsoft+corp..html"><![CDATA[Microsoft Corp.]]></category>
		<category domain="http://resources.bnet.com/topic/microsoft+excel.html"><![CDATA[Microsoft Excel]]></category>
		<category domain="http://resources.bnet.com/topic/forecasting.html"><![CDATA[Forecasting]]></category>
		<category domain="http://resources.bnet.com/topic/analyzerxl.html"><![CDATA[AnalyzerXL]]></category>
		<category domain="http://resources.bnet.com/topic/neuroxl+predictor.html"><![CDATA[NeuroXL Predictor]]></category>
		<category domain="http://resources.bnet.com/topic/sales+force+management.html"><![CDATA[Sales Force Management]]></category>
		<category domain="http://resources.bnet.com/topic/sales.html"><![CDATA[Sales]]></category>
		<category domain="http://rss.financialcontent.com/stocksymbol">MSFT</category>
		<category domain="tickers">MSFT</category>
	</item>
	<item>
		<title><![CDATA[Introduction to the Development of Methodologies for Independent Verification and Validation of Neural Networks]]></title>
		<link><![CDATA[http://jobfunctions.bnet.com/abstract.aspx?docid=163845]]></link>
		<description><![CDATA[The use of Artificial Neural Networks ANNs within the NASA applications is expected to increase over the next few decades. Currently, there are over 20 NASA funded activities that use Neural Network NN technology. High criticality software applications of NNs will require a rigorous Verification and Validation (V&V) process. No...]]></description>
		<s:doctype><![CDATA[White papers]]></s:doctype>
		<pubDate>Fri, 14 Feb 2003 00:00:00 -0800</pubDate>
		<category domain="http://resources.bnet.com/topic/network.html"><![CDATA[Network]]></category>
		<category domain="http://resources.bnet.com/topic/neural+network.html"><![CDATA[Neural Network]]></category>
		<category domain="http://resources.bnet.com/topic/institute+for+scientific+research.html"><![CDATA[Institute For Scientific Research]]></category>
	</item>
	<item>
		<title><![CDATA[Extrusion Die Design: A New Methodology of Using Design of Experiments as a Precursor to Neural Networks]]></title>
		<link><![CDATA[http://jobfunctions.bnet.com/abstract.aspx?docid=60357]]></link>
		<description><![CDATA[This article describes a new methodology of using design of experiments as a precursor to identify the importance of some variables and, thus, reduce the data set needed for training a neural network. Based on the design-of-experiments results, a neural-network training set is generated with more variations for the most...]]></description>
		<s:doctype><![CDATA[White papers]]></s:doctype>
		<pubDate>Wed, 01 Jan 2003 00:00:00 -0800</pubDate>
		<category domain="http://resources.bnet.com/topic/network.html"><![CDATA[Network]]></category>
		<category domain="http://resources.bnet.com/topic/neural+network.html"><![CDATA[Neural Network]]></category>
		<category domain="http://resources.bnet.com/topic/training.html"><![CDATA[Training]]></category>
		<category domain="http://resources.bnet.com/topic/workforce+management.html"><![CDATA[Workforce Management]]></category>
		<category domain="http://resources.bnet.com/topic/training+and+certification.html"><![CDATA[Training And Certification]]></category>
		<category domain="http://resources.bnet.com/topic/human+resources.html"><![CDATA[Human Resources]]></category>
	</item>
	<item>
		<title><![CDATA[An analysis of a hybrid neural network and pattern recognition technique for predicting short-term increases in the NYSE composite index.(Statistical Data Included)]]></title>
		<link><![CDATA[http://findarticles.com/p/articles/mi_hb4915/is_200204/ai_n18046676]]></link>
		<description><![CDATA[We introduce a method for combining template matching, from pattern recognition, and the feed-forward neural network, from artificial intelligence, to forecast stock market activity. We evaluate the effectiveness of the method for forecasting increases   We introduce a method for combining template matching, from pattern recognition, and the feed-forward...]]></description>
		<s:doctype><![CDATA[Research articles]]></s:doctype>
		<pubDate>Mon, 01 Apr 2002 00:00:00 -0800</pubDate>
		<category domain="http://resources.bnet.com/topic/pattern+recognition.html"><![CDATA[pattern recognition]]></category>
		<category domain="http://resources.bnet.com/topic/neural+network.html"><![CDATA[neural network]]></category>
		<category domain="http://resources.bnet.com/topic/forecasting.html"><![CDATA[forecasting]]></category>
		<category domain="http://resources.bnet.com/topic/network.html"><![CDATA[network]]></category>
		<category domain="http://resources.bnet.com/topic/nyse+euronext.html"><![CDATA[NYSE Euronext]]></category>
		<category domain="http://resources.bnet.com/topic/analysis.html"><![CDATA[analysis]]></category>
	</item>
	<item>
		<title><![CDATA[Lithological Classification by Drilling]]></title>
		<link><![CDATA[http://jobfunctions.bnet.com/abstract.aspx?docid=119865]]></link>
		<description><![CDATA[This white paper research is exploring intelligent drilling that can be applied to multiple applications. The paper reveals that the methodology uses a neural network to classify material lithology where the inputs to the neural network are sensed drill parameters such as thrust, torque, rotary speed and penetration rate, as...]]></description>
		<s:doctype><![CDATA[White papers]]></s:doctype>
		<pubDate>Fri, 08 Feb 2002 00:00:00 -0800</pubDate>
		<category domain="http://resources.bnet.com/topic/parameter.html"><![CDATA[Parameter]]></category>
		<category domain="http://resources.bnet.com/topic/carnegie-mellon+university.html"><![CDATA[Carnegie-Mellon University]]></category>
		<category domain="http://resources.bnet.com/topic/neural+network.html"><![CDATA[Neural Network]]></category>
	</item>
	<item>
		<title><![CDATA[Use of Recurrent Neural Networks for Strategic Data Mining of Sales]]></title>
		<link><![CDATA[http://jobfunctions.bnet.com/abstract.aspx?docid=68671]]></link>
		<description><![CDATA[An increasing number of organizations are involved in the development of strategic information systems for effective linkages with their suppliers, customers, and other channel partners involved in transportation, distribution, warehousing and maintenance activities. An efficient inter-organizational inventory management system based on data mining techniques is a significant step in this...]]></description>
		<s:doctype><![CDATA[White papers]]></s:doctype>
		<pubDate>Fri, 01 Feb 2002 00:00:00 -0800</pubDate>
		<category domain="http://resources.bnet.com/topic/network.html"><![CDATA[Network]]></category>
		<category domain="http://resources.bnet.com/topic/social+science+electronic+publishing+inc..html"><![CDATA[Social Science Electronic Publishing Inc.]]></category>
		<category domain="http://resources.bnet.com/topic/neural+network.html"><![CDATA[Neural Network]]></category>
		<category domain="http://resources.bnet.com/topic/sales.html"><![CDATA[Sales]]></category>
		<category domain="http://resources.bnet.com/topic/data+mining.html"><![CDATA[Data Mining]]></category>
		<category domain="http://resources.bnet.com/topic/business+intelligence.html"><![CDATA[Business Intelligence]]></category>
		<category domain="http://resources.bnet.com/topic/marketing+research.html"><![CDATA[Marketing Research]]></category>
		<category domain="http://resources.bnet.com/topic/databases.html"><![CDATA[Databases]]></category>
		<category domain="http://resources.bnet.com/topic/enterprise+software.html"><![CDATA[Enterprise Software]]></category>
		<category domain="http://resources.bnet.com/topic/software.html"><![CDATA[Software]]></category>
		<category domain="http://resources.bnet.com/topic/data+management.html"><![CDATA[Data Management]]></category>
		<category domain="http://resources.bnet.com/topic/marketing.html"><![CDATA[Marketing]]></category>
	</item>
	<item>
		<title><![CDATA[Combining Belief Functions And Neural Networks To Assess The Likelihood Of Fraud: The Case of Commercial Bank Audits]]></title>
		<link><![CDATA[http://jobfunctions.bnet.com/abstract.aspx?docid=57654]]></link>
		<description><![CDATA[When assessing the likelihood of fraud in commercial banks, an auditor is faced with two related issues: determining significant red flags in the commercial banking industry, and combining red flags in a model Decision Aid based on weights Values of uncertainties assigned to them. Prior research largely ignores the first...]]></description>
		<s:doctype><![CDATA[White papers]]></s:doctype>
		<pubDate>Tue, 01 Jan 2002 00:00:00 -0800</pubDate>
		<category domain="http://resources.bnet.com/topic/audit.html"><![CDATA[Audit]]></category>
		<category domain="http://resources.bnet.com/topic/network.html"><![CDATA[Network]]></category>
		<category domain="http://resources.bnet.com/topic/flag.html"><![CDATA[Flag]]></category>
		<category domain="http://resources.bnet.com/topic/neural+network.html"><![CDATA[Neural Network]]></category>
		<category domain="http://resources.bnet.com/topic/commercial+bank.html"><![CDATA[Commercial Bank]]></category>
		<category domain="http://resources.bnet.com/topic/fraud.html"><![CDATA[Fraud]]></category>
		<category domain="http://resources.bnet.com/topic/litigation.html"><![CDATA[Litigation]]></category>
		<category domain="http://resources.bnet.com/topic/financial+accounting.html"><![CDATA[Financial Accounting]]></category>
		<category domain="http://resources.bnet.com/topic/business+operations.html"><![CDATA[Business Operations]]></category>
		<category domain="http://resources.bnet.com/topic/finance.html"><![CDATA[Finance]]></category>
	</item>
	<item>
		<title><![CDATA[Semantic Disturbance in Schizophrenia and Its Relationship to the Cognitive Neuroscience of Attention]]></title>
		<link><![CDATA[http://jobfunctions.bnet.com/abstract.aspx?docid=123699]]></link>
		<description><![CDATA[This paper views schizophrenia as producing a failure of attentional modulation that leads to a breakdown in the selective enhancement or inhibition of semantic/lexical representations whose biological substrata are widely distributed across left dominant temporal and frontal lobes. Supporting behavioral evidence includes word recall studies that have pointed to a...]]></description>
		<s:doctype><![CDATA[White papers]]></s:doctype>
		<pubDate>Wed, 04 Jul 2001 00:00:00 -0700</pubDate>
		<category domain="http://resources.bnet.com/topic/reed+elsevier+inc..html"><![CDATA[Reed Elsevier Inc.]]></category>
		<category domain="http://resources.bnet.com/topic/network.html"><![CDATA[Network]]></category>
		<category domain="http://resources.bnet.com/topic/neural+network.html"><![CDATA[Neural Network]]></category>
		<category domain="http://resources.bnet.com/topic/disturbance.html"><![CDATA[Disturbance]]></category>
		<category domain="http://resources.bnet.com/topic/networking.html"><![CDATA[Networking]]></category>
	</item>
	<item>
		<title><![CDATA[Artificial Neural Networks for Valuation of Financial Derivatives and Customized Option Embedded Contracts]]></title>
		<link><![CDATA[http://jobfunctions.bnet.com/abstract.aspx?docid=101880]]></link>
		<description><![CDATA[In this paper a proposal and test valuation methodology for improving the efficiency of contingent claims pricing using Artificial Neural Networks ANN. A contingent claim is by now a standard method for pricing under uncertainty nonlinear option embedded contracts, for both financial options standardized or customized and real investment opportunities....]]></description>
		<s:doctype><![CDATA[White papers]]></s:doctype>
		<pubDate>Tue, 01 May 2001 00:00:00 -0700</pubDate>
		<category domain="http://resources.bnet.com/topic/valuation.html"><![CDATA[Valuation]]></category>
		<category domain="http://resources.bnet.com/topic/financial.html"><![CDATA[Financial]]></category>
		<category domain="http://resources.bnet.com/topic/network.html"><![CDATA[Network]]></category>
		<category domain="http://resources.bnet.com/topic/neural+network.html"><![CDATA[Neural Network]]></category>
		<category domain="http://resources.bnet.com/topic/pricing+strategy.html"><![CDATA[Pricing Strategy]]></category>
		<category domain="http://resources.bnet.com/topic/university+of+cyprus.html"><![CDATA[University Of Cyprus]]></category>
		<category domain="http://resources.bnet.com/topic/investment.html"><![CDATA[Investment]]></category>
		<category domain="http://resources.bnet.com/topic/financial+accounting.html"><![CDATA[Financial Accounting]]></category>
		<category domain="http://resources.bnet.com/topic/finance.html"><![CDATA[Finance]]></category>
	</item>
	<item>
		<title><![CDATA[Modeling Consideration Sets And Brand Choice Using Artificial Neural Networks]]></title>
		<link><![CDATA[http://jobfunctions.bnet.com/abstract.aspx?docid=77162]]></link>
		<description><![CDATA[The concept of consideration sets makes brand choice a two-step process. Households first construct a consideration set which not necessarily includes all available brands and conditional on this set they make a final choice. This paper puts forward a parametric econometric model for this two-step process, where consideration sets usually...]]></description>
		<s:doctype><![CDATA[White papers]]></s:doctype>
		<pubDate>Mon, 31 Jul 2000 00:00:00 -0700</pubDate>
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		<title><![CDATA[Halliburton Unit Receives Patent Based On BioComp Systems' Intelligent Neural Networks to Help Boost Oil and Gas Production]]></title>
		<link><![CDATA[http://findarticles.com/p/articles/mi_m0EIN/is_2000_May_23/ai_62264209]]></link>
		<description><![CDATA[Business Editors]]></description>
		<s:doctype><![CDATA[Research articles]]></s:doctype>
		<pubDate>Tue, 23 May 2000 00:00:00 -0700</pubDate>
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