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Two versions of component video exist: interlaced-scan and progressivescan Progressive-scan component video produces a picture with significantly more detail than interlaced-scan component video and requires a progressive-scan display Not all DVD players provide component video output, very few televisions have component video connections, and very few receivers or A/V controllers can switch component video inputs There are even fewer progressive players and progressive TVs Connecting Component Video Some DVD players, notably US and Japanese models, have YPbPr component video output in the form of three RCA or BNC connectors The connectors may be labeled Y, U, and V, or Y, Pb, and Pr, or Y, B-Y, and R-Y 1 The connectors may be colored green, blue, and red, respectively Some DVD players, notably European models, have RGB component video output via a SCART connector or via three RCA or BNC connectors The RGB connectors are generally labeled R, G, and B and may be colored to match Hook a SCART cable from the player to the video system, or hook three video cables from the three video outputs of the player to the three video inputs of the video system S-Video All DVD players have s-video (Y/C) output, which generally gives a better picture than composite video output unless the s-video cable is very long In most cases, the picture from an s-video connection is distinctly better than from a composite connection and is only slightly inferior to a component connection S-video provides more detail, better color, and less color bleeding than composite video The advantage of s-video is that the luma (Y) and chroma (C) signals are carried separately Because they are stored independently on the disc, the best results are achieved when they are not combined by the player and then reseparated by a comb (or similar) filter in the TV S-video is sometimes erroneously referred to as S-VHS because it was popularized by S-VHS VCRs Note that most low-end A/V receivers are unable to switch s-video signals, or only provide it on a few inputs Connecting S-Video Hook an s-video cable from the player to the video system The round, 4-pin connectors maybe labeled Y/C, s-video, or S-VHS.

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Many DVD players label the YPbPr connectors as YCbCr This is incorrect because YCbCr refers only to digital component video signals, not analog component video signals

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Craven, M W, & Shavlik, J W (1994) Using sampling and queries to extract rules from trained neural networks Proceedings of the 11th International Conference on Machine Learning (pp 3745) San Mateo, CA: Morgan Kaufmann Fu, L M (1989) Integration of neural heuristics into knowledge-based inference ConnectionScience, 1(3), 325-339 Fu, L M (1993) Knowledge-based connectionism for revising domain theories IEEE Transactions on Systems, Man, and Cybernetics, 23(1), 173-182 Gallant, S I (1988) Connectionist expert systems CACM, 31(2), 152-169 Koppel, M, Feldman, R, & Segre, A (1994) Bias-driven revision of logical domain theories Journal of Artificial Intelligence, 1, 159-208 http:llwwwcswashingtonedulresearch/jairhomehtml Lacher, R, Hmska, S, & Kuncicky, D (1991) Backpropagation learning in expert networks (Dept of Computer Science Technical Report TR91-015) Florida State University, Tallahassee Mach, R, & Shavlik, J (1993) Using knowledge-based neural networks to improve algorithms: Refining the Chou-Fasman algorithm for protein folding Machine Learning, 11(3), 195-215 Mitchell, T M, & Thrun, S B (1993a) Explanation-based neural network learning for robot control In S Hanson, J Cowan, & C Giles (Eds), Advances in neural infomtionprocessing systems 5 (pp 287-294) San Mateo, CA: Morgan-Kaufmann Press Mitchell, T M, & Thrun, S B (1993b) Explanation-based learning: A comparison of symbolic and neural network approaches Tenth International Conference on Machine Learning, Amherst, MA Mooney, R (1993) Induction over the unexplained: Using overly-general domain theories to aid concept learning Machine Learning, lO(1) O'Sullivan, J, Mitchell, T, & Thrun, S (1997) Explanation-based learning for mobile robot perception In K Ikeuchi & M Veloso (Eds), Symbolic Visual Learning (pp 295-324) Ourston, D, & Mooney, R J (1994) Theory refinement combining analytical and empirical methods Arti2cial Intelligence, 66(2) Pazzani, M J, & Brunk, C (1993) Finding accurate frontiers: A knowledge-intensive approach to relational learning Proceedings of the I993 National Conference on Artificial Intelligence (pp 328-334) AAAI Press Pazzani, M J, Brunk, C A, & Silverstein, G (1991) A knowledge-intensive approach to learning relational concepts Proceedings of the Eighth International Workshop on Machine Learning (pp 432436) San Mateo, CA: Morgan Kaufmann Pazzani, M J, & Kibler, D (1992) The utility of knowledge in inductive learning MachineLearning, 9(1), 57-94 neural Pratt, L Y (1993a) Transferring previously learned BACKPROPAGATION networks to new learning tasks (PhD thesis) Department of Computer Science, Rutgers University, New Jersey (Also Rutgers Computer Science Technical Report ML-TR-37) Pratt, L Y (1993b) Discriminability-based transfer among neural networks In J E Moody et al (Eds), Advances in Nerual Infomtion Processing Systems 5 San Mateo, CA: Morgan Kaufmann Rosenbloom, P S, & Aasman, J (1990) Knowledge level and inductive uses of chunking (ebl) Proceedings of the Eighth National Conference on Artificial Intelligence (pp 821-827) AAAI Press Russell, S, Binder, J, Koller, D, & Kanazawa, K (1995) Local learning in probabilistic networks with hidden variables Proceedings of the 14th International Joint Conference on Artificial Intelligence, Montreal Morgan Kaufmann Shavlik, J, & Towell, G (1989) An approach to combining explanation-based and neural learning algorithms Connection Science, 1(3), 233-255 Simard, P S, Victoni, B, LeCun, Y, & Denker, J (1992) Tangent prop-A formalism for specifying selected invariances in an adaptive network In J Moody et al (Eds), Advances in Neural Inforination Processing System 4 San Mateo, CA: Morgan Kaufmann Sudharth, S C, & Holden, A D C (1991) Symbolic-neural systems and the use of hints for developing complex systems International Journal of Man-Machine Studies, 35(3), 291-3 11.

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