REFERENCES in Visual Studio .NET

Generate Data Matrix 2d barcode in Visual Studio .NET REFERENCES
REFERENCES
ECC200 Recognizer In VS .NET
Using Barcode Control SDK for Visual Studio .NET Control to generate, create, read, scan barcode image in .NET framework applications.
281. J. R. Quinlan. C4.5: Programs for Machine Learning. San Mateo, CA: Morgan Kaufmann Publishers. 1993. 282. A. M. Radzikowska and E. E. Kerre. A comparative study of fuzzy rough sets. Fuzzy Sets Sys. 126(2): 137 155. 2002. 283. A. M. Radzikowska and E. E. Kerre. Fuzzy rough sets based on residuated lattices. In Transactions on Rough Sets II. Berlin: Springer, pp. 278 296. 2004. 284. R. Rallo, J. Ferr -Gin , and F. Giralt. Best feature selection and data completion e e for the design of soft neural sensors. In Proceedings of AIChE 2003, 2nd Topical Conference on Sensors, San Francisco. 2003. 285. B. Raman and T.R. Ioerger. Instance-based lter for feature selection. J. Machine Learn. Res. 1: 1 23. 2002. 286. K. Rasmani and Q. Shen. Modifying weighted fuzzy subsethood-based rule models with fuzzy quanti ers. In Proceedings of 13th International Conference on Fuzzy Systems, NJ: IEEE Press, pp. 1687 1694. 2004. 287. K. Rasmani and Q. Shen. Data-driven fuzzy rule generation and its application for student academic performance evaluation. Appl. Intell. 25(3): 305 319. 2006. 288. T. Rauma. Knowledge acquisition with fuzzy modeling. In Proceedings of 5th IEEE International Conference on Fuzzy Systems, Vol. 3. Piscataway, NJ: IEEE Press, pp. 1631 1636. 1996. 289. M. W. Richardson. Multidimensional psychophysics. Psycholog. Bull. 35: 659 660. 1938. 290. W. Romao, A. A. Freitas, and R. C. S. Pacheco. A genetic algorithm for discovering interesting fuzzy prediction rules: Applications to science and technology data. In Proceedings of the Genetic and Evolutionary Computation Conference. Morgan Kaufmann, pp. 343 350. 2002. 291. The ROSETTA homepage. Available at http://rosetta.lcb.uu.se/general/. 292. RSES: Rough Set Exploration System. Available at http://logic.mimuw.edu.pl/ rses/. 293. S. T. Roweis and L. K. Saul. Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500): 2323 2326. 2000. 294. M. Ruggiero. Turning the key. Futures 23(14): 38 40. 1994. 295. M. E. Ruiz and P. Srinivasan. Hierarchical neural networks for text categorization. In Proceedings of SIGIR-99, 22nd ACM International Conference on Research and Development in Information Retrieval . NY: ACM, pp. 281 282. 1999. 296. D. Rumelhant, E. Hinton, and R. Williams. Learning internal representations by error propagating. In E. Rumelhant and J. McCkekkand, eds., Parallel Distributed Processing. Cambridge: MIT Press. 1986. 297. T. Runkler. Selection of appropriate defuzzi cation methods using application speci c properties. IEEE Trans. Fuzzy Sys. 5(1): 72 79. 1997. 298. S. Russell and P. Norvig. Arti cial Intelligence: A Modern Approach. Englewood Cliffs, NJ: Prentice Hall. 1995. 299. Y. Saeys, S. Degroeve, D. Aeyels, P. Rouze, and Y. Van De Peer. Feature selection for splice site prediction: A new method using EDA-based feature ranking. BMC Bioinform. 5(64): 2004.
Data Matrix ECC200 Creator In VS .NET
Using Barcode generation for .NET Control to generate, create Data Matrix 2d barcode image in .NET applications.
REFERENCES
Data Matrix ECC200 Recognizer In .NET
Using Barcode decoder for .NET Control to read, scan read, scan image in .NET applications.
300. G. Salton, A. Wong, and C.S. Yang. A vector space model for automatic indexing. Comm. ACM 18(11): 613 620. 1975. 301. G. Salton, E. A. Fox, and H. Wu. Extended Boolean information retrieval. Comm. ACM 26(12): 1022 1036. 1983. 302. G. Salton, Introduction to Modern Information Retrieval. New York: McGraw-Hill. 1983. 303. G. Salton, and C. Buckley. Term weighting approaches in automatic text retrieval. Technical report TR87-881. Department of Computer Science, Cornell University. 1987. 304. M. Sarkar. Fuzzy-rough nearest neighbors algorithm. In Proceedings of the IEEE International Conference on Systems, Man and Cybernetics. Piscataway, NJ: IEEE Press, pp. 3556 3561. 2000. 305. M. Sarkar. Ruggedness measures of medical time series using fuzzy-rough sets and fractals. Pattern Recog. Lett. 27(5): 447 454. 2006. 306. J. C. Schlimmer. Ef ciently inducing determinations A complete and systematic search algorithm that uses optimal pruning. International Conference on Machine Learning. Morgan Kaufmann, pp. 284 290. 1993. 307. B. Sch lkopf. Support Vector Learning. Munich: Oldenbourg Verlag. 1997. o 308. M. Schroeder. Fractals, Chaos, Power Laws: Minutes from an In nite Paradise. New York: Freeman. 1991. 309. F. Sebastiani. Machine learning in automated text categorisation. ACM Comput. Sur. 34(1): 1 47. 2002. 310. M. Sebban and R. Nock. A hybrid lter/wrapper approach of feature selection using information theory. Pattern Recog. 35(4): 835 846. 2002. 311. B. Selman and H. A. Kautz. Domain-independant extensions to GSAT: Solving large structured variables. In Proceedings of 13th International Joint Conference on Arti cial Intelligence. Springer, pp. 290 295. 1993. 312. R. Setiono and H. Liu. Neural network feature selector. IEEE Trans. Neural Net. 8(3): 645 662. 1997. 313. M. Setnes and H. Roubos. GA-fuzzy modeling and classi cation: Complexity and performance. IEEE Trans. Fuzzy Sys. 8(5): 509 522. 2000. 314. H. Sever. The status of research on rough sets for knowledge discovery in databases. In Proceedings of 2nd International Conference on Nonlinear Problems in Aviation and Aerospace, Cambridge: European Conference Publications, Vol. 2. pp. 673 680. 1998. 315. G. Shafer. A Mathematical Theory of Evidence. Princeton: Princeton University Press. 1976. 316. D. Shan, N. Ishii, Y. Hujun, N. Allinson, R. Freeman, J. Keane, and S. Hubbard. Feature weights determining of pattern classi cation by using a rough genetic algorithm with fuzzy similarity measure. In Proceedings of the Intelligent Data Engineering and Automated Learning. Springer pp. 544 550, 2002. 317. C. Shang and Q. Shen. Rough feature selection for neural network based image classi cation. Int. J. Image Graph. 2(4): 541 556. 2002. 318. C. Shang and Q. Shen. Aiding classi cation of gene expression data with feature selection: A comparative study. Comput. Intell. Res. 1(1): 68 76. 2006.
Generate Bar Code In VS .NET
Using Barcode creation for .NET Control to generate, create barcode image in Visual Studio .NET applications.
Reading Bar Code In .NET Framework
Using Barcode scanner for Visual Studio .NET Control to read, scan read, scan image in Visual Studio .NET applications.
Data Matrix Encoder In .NET
Using Barcode maker for ASP.NET Control to generate, create ECC200 image in ASP.NET applications.
Creating Bar Code In .NET
Using Barcode maker for Visual Studio .NET Control to generate, create barcode image in Visual Studio .NET applications.
Printing Data Matrix In Visual Studio .NET
Using Barcode drawer for .NET Control to generate, create Data Matrix 2d barcode image in .NET applications.
Make EAN-13 Supplement 5 In Visual Studio .NET
Using Barcode maker for ASP.NET Control to generate, create European Article Number 13 image in ASP.NET applications.
Scanning EAN 13 In .NET Framework
Using Barcode recognizer for Visual Studio .NET Control to read, scan read, scan image in Visual Studio .NET applications.
Generating UPC Code In C#.NET
Using Barcode printer for .NET framework Control to generate, create GS1 - 12 image in .NET framework applications.
EAN128 Creator In Java
Using Barcode creator for Java Control to generate, create EAN 128 image in Java applications.