At least that’s the lemonade made from his lemon of a low-cost vector network analyzer.įor the uninitiated, a VNA is a versatile test instrument for RF work that allows you to measure both the amplitude and the phase of a signal, and it can be used for everything from antenna and filter design to characterizing transmission lines. A complex project with a lot of subsystems has a greater chance of at least partial success, as well as providing valuable lessons in what not to do next time. Estimation of Dependences Based on Empirical Data, Addendum 1, New York: Springer-Verlag.If you’re going to fail, you might as well fail ambitiously. Rumelhart (Eds.), Parallel Distributed Processing, 1, 318–362, MIT Press.
Learning internal representations by error propagation.
Rumelhart, D.E., Hinton, G.E., & Williams, R.J. Learning internal representations by backpropagating errors. Principles of Neurodynamics, Spartan Books, New York. Technical Report TR-47, Center for Computational Research in Economics and Management Science, Massachusetts Institute of Technology, Cambridge, MA. Advances in Neural Information Processing Systems, 2, 396–404, Morgan Kaufman. Handwritten digit recognition with a back-propagation network. LeCun, Y., Boser, B., Denker, J.S., Henderson, D., Howard, R.E., Hubbard, W., & Jackel, L.D. Cognitiva 85: A la Frontiere de l'Intelligence Artificielle des Sciences de la Connaissance des Neurosciences, 599–604, Paris. Une procedure d'apprentissage pour reseau a seuil assymetrique. The use of multiple measurements in taxonomic problems. Methods of Mathematical Physics, Interscience, New York.įisher, R.A. Neural-network and k-nearest-neighbor classifiers. Proceedings of 12th International Conference on Pattern Recognition and Neural Network.īromley, J., & Sackinger, E. Comparison of classifier methods: A case study in handwritten digit recognition. Pittsburgh, ACM.īottou, L., Cortes, C., Denker, J.S., Drucker, H., Guyon, I., Jackel, L.D., LeCun, Y., Sackinger, E., Simard, P., Vapnik, V., & Miller, U.A. In Proceedings of the Fifth Annual Workshop of Computational Learning Theory, 5, 144–152. A training algorithm for optimal margin classifiers. Classification into two multivariate normal distributions with different covariance matrices. Automation and Remote Control, 25:821–837.Īnderson, T.W., & Bahadur, R.R.
Theoretical foundations of the potential function method in pattern recognition learning. Aizerman, M., Braverman, E., & Rozonoer, L.
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