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1992] W Sverdlik, RG Reynolds, E Zannoni, HYBAL: A Self-Tuning Algorithm for Concept earning in Highly Autonomous Systems, Proceedings of the Third Annual Conference on Artificial Intelligence, Simulation, and Planning in High Autonomy Systems, IEEE Computer Society Press, 1992, pp 15 22 [Syswerda 1991] G Syswerda, Schedule Optimization using Genetic Algorithms, in L Davis (ed), Handbook of Genetic Algorithms, Van Nostrand Reinhold, 1991, pp 332 349 [Takagi and Sugeno 1985] T Takagi, M Sugeno, Fuzzy Identification of Systems and its Application to Modeling and Control, IEEE Transactions on Systems, Man, and Cybernetics, Vol 15, No 1, 1985, pp 116 132 [Takahashi 1993] T Takahashi, Principal Component Analysis is a Group Action of SO(N) which Minimizes an Entropy Function, Proceedings of the 1993 International Joint Conference on Neural Networks, Vol 1, 1993, pp 355 358 [Tamura et al.

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In the business world, almost everywhere one looks new technology abounds computers, printers, scanners, personal digital assistants, and mobile phones, plus networks for these devices to plug into Why is this First, since 1995 the United States has experienced dramatic improvements in productivity Although information technology s contribution continues to be debated, evidence suggests that it is responsible for at least some of the effect (Federal Reserve Bank of San Francisco, 2000; McKinsey & Company, 2001; Oliner & Sichel, 2000) Technology may help increase productivity because manipulating information electronically is often cheaper than physically manipulating things (Negroponte, 1995) The following are examples: Southwest Airlines expends $10 to sell a ticket through a travel agent, while it pays $1 to make an online sale (Abate 2001) Because Southwest gets 30% of its passenger revenue through online sales, the savings are enormous GE purchases large amounts of supplies and raw materials through online exchanges, generating an estimated savings of $600 million through lower prices and cheaper transaction costs (De Meyer & Steinberg, 2001) Wal-Mart has extensive databases that give real-time information on individual store inventories to the chain s 10,000 suppliers, who can re ll inventory as needed (Steinberg, 2001) Among other things, this system saves Wal-Mart the cost of buying and storing more inventory than it can quickly sell Cisco Systems must train thousands of sales and technical support staff each time a new product is launched Since these employees are deployed worldwide, the travel costs alone are huge Through e-learning, Cisco has reported savings of 40 60% over instructor-led courses (Cisco Systems, 2001) Besides aiding ef ciency, technology can break down traditional barriers, enhancing the value of products and services One such barrier was the ageold relationship between richness and reach (Evans & Wurster, 2000) It used to be that a business could either reach many customers with a relatively spare message say, through broadcast advertising or engage a few customers in a deeper interaction (eg via a showroom encounter) The Internet has fundamentally changed that traditional relationship by permitting companies to reach many individuals in a personalized way Customers can use the Web to order a computer built to particular speci cations (http:/ /wwwdellcom), design their own sneakers (http:/ /wwwcustomatixcom), con gure of ce furniture to suit their needs (http:/ /wwwhermanmillercom), or purchase pants to t (http:/ /wwwlandsendcom) The value that technology provides to businesses and consumers is continually increasing One illustration is Moore s law: since the late 1960s, computational capability has doubled every 1 2 years (Kurzweil, 1999; Negroponte, 1995) The impact has been to dramatically expand the tasks computers can.

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1993] S Tamura, M Tateishi, M Matumoto, S Akita, Determination of the Number of Redundant Hidden Units in a Three-layer Feed-Forward Neural Network, Proceedings of the 1993 International Joint Conference on Neural Networks, Vol 1, 1993, pp 335 338 [Teller 1994] A Teller, The Evolution of Mental Models, in KE Kinnear (ed), Advances in Genetic Programming, MIT Press, 1994, pp 199 219 [Thiele 1998] H Thiele, Fuzzy Rough Sets versus Rough Fuzzy Sets -An Interpretation and a Comparative Study using Concepts of Modal Logics, Technical Report CI-30/98, University of Dortmund, April 1998 [Thodberg 1991] HH Thodberg, Improving Generalization of Neural Networks through Pruning, International Journal of Neural Systems, Vol 1, No 4, 1991, pp 317 326 [Turing 1950] AM Turing, Computing Machinery and Intelligence, Mind, Vol 59 1950, pp 433 460.

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[Van den Bergh 1999] F van den Bergh, Particle Swarm Weight Initialization in Multi-Layer Perceptron Artificial Neural Networks, Proceedings of the International Conference on Artificial Intelligence, 1999, pp 41 45 [Thiele 1998] H Thiele, Fuzzy Rough Sets versus Rough Fuzzy Sets -An Interpretation and a Comparative Study using Concepts of Modal Logics, Technical Report CI-30/98 University of Dortmund, April 1998 [Thodberg 1991] HH Thodberg, Improving Generalization of Neural Networks through Pruning, International Journal of Neural Systems, Vol 1, No 4, 1991, pp 317 326 [Turing 1950] AM Turing, Computing Machinery and Intelligence, Mind, Vol 59 1950, pp 433 460 [Van den Bergh 1999] F van den Bergh, Particle Swarm Weight Initialization in Multi-Layer Perceptron Artificial Neural Networks, Proceedings of the International Conference on Artificial Intelligence, 1999, pp 41 45 to encode barcode 39 and uss code 39 data, size Related: Code 128 Creating NET , UPC-A Generation NET , PDF417 Printing VBNET.

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[341] K Hadeli, P Valckenaers, M Kollingbau, and H Van Brussel Multi-Agent Coordination and Control using Stigmergy Computers in Industry, 53(1):75 96, 2004 [342] M Hagiwara Removal of Hidden Units and Weights for Back Propagation Networks In Proceedings of the International Joint Conference on Neural Networks, volume 1, pages 351 354, 1993 [343] H Handa, N Baba, O Katai, T Sawaragi, and T Horiuchi Genetic Algorithm Involving Coevolution echanism to Search for E ective Genetic Information In Proceedings of the IEEE International Conference on Evolutionary Computation, pages 709 714, 1997 [344] J Handl, J Knowles, and M Dorigo Ant-Based Clustering: A Comparative Study of Its Relative Performance with Respect to k-Means, Average Link and 1D-SOM Technical Report TR/IRIDIA/2003-24, Universit Libre de Bruxelles, e 2003 [345] J Handl and B Meyer Improved Ant-Based Clustering and Sorting in a Document Retrieval Interface In Proceedings of the Seventh International Conference on Parallel Problem Solving from Nature, Lecture Notes in Computer Science, volume 2439, pages 913 923 Springer-Verlag, 2002 [346] SJ Hanson and LY Pratt Comparing Biases for Minimal Network Construction with Back-Propagation In DS Touretzky, editor, Advances in Neural Information Processing Systems, volume 1, pages 177 185, 1989 [347] S Harding and JF Miller Evolution of Robot Controller Using Cartesian Genetic Programming In Lecture Notes in Computer Science, volume 3447, pages 62 73, 2005 [348] AG Hart and FLW Ratnieks Task Partitioning, Division of Labour and Nest Compartmentalisation Collectively Isolate Hazardous Waste in the Leafcutting Ant Atta Cephalotes Behavioral Ecology and Sociobiology, 49:387 392, 2001 [349] EF Hartman, JD Keeler, and JM Kowalski Layered Neural Networks with Gaussian Hidden Units as Universal Approximators Neural Computation, 2(2):210 215, 1990 [350] Y Hasegawa, K Mase, and T Fukuda Re-Grasping Behavior Acquisition by Evolutionary Programming In Proceedings of the IEEE Congress on Evolutionary Computation, volume 1, 1999 [351] B Hassibi and DG Stork Second Order Derivatives for Network Pruning: Optimal Brain Surgeon In C Lee Giles, SJ Hanson, and JD Cowan, editors, Advances in Neural Information Processing Systems, volume 5, pages 164 171, 1993 [352] B Hassibi, DG Stork, and G Wol Optimal Brain Surgeon: Extensions and Performance Comparisons In JD Cowan, G Tesauro, and J Alspector, editors, Advances in Neural Information Processing Systems, volume 6, pages 263 270, 1994 [353] D Haussler, M Kearns, M Opper, and R Schapire Estimating Average-Case Learning Curves using Bayesian, Statistical Physics and VC Dimension Method In J Moody, SJ Hanson, and R Lippmann, editors, Advances in Neural Information Processing Systems, volume 4, pages 855 862, 1992.

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Using Various C# Methods to Convert TIFF to PNG - Yiigo
With a few simple steps, users can easily convert TIFF to PNG in C#.NET. First, please load the TIFF file you are going to convert. The loaded TIFF file can be viewed in the thumbnail preview area. Second, choose the target image format, or you can directly save TIFF file to gif format.

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