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lsm

于 2007-03-28 发布文件大小:1KB

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是lsm算法的一个例子，可以实现图形，对学习自适应的有一定的帮助(lsm algorithm is an example, can achieve graphics, the adaptive learning can help)

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Supported Languages: Afrikaans, Albanian, Arabic, Azerbaijani, Basque, Belarusian, Bengali, Bulgarian, Catalan, Cherokee, Chinese (Simplified and Traditional), Croatian, Czech, Danish, Dutch, English, Esperanto, Estonian, Finnish, Frankish, French, Galician, German, Greek (Ancient and Modern), Hebrew, Hindi, Hungarian, Icelandic, Indonesian, Italian (Ancient and Modern), Japanese, Kannada, Korean, Latvian, Lithuanian, Macedonian, Malay, Malayalam, Maltese, Middle English, Middle French, Norwegian, Polish, Portuguese, Romanian, Russian, Serbian, Slovakian, Slovenian, Spanish (Ancient and Modern), Swahili, Swedish, Tagalog, Tamil, Telugu, Thai, Turkish, Ukrainian, Vietnamese

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Vigenere-Key-Method
The Vigenère cipher is a method of encrypting alphabetic text by using a series of different Caesar ciphers based on the letters of a keyword. It is a simple form of polyalphabetic substitution.Though the cipher is easy to understand and implement, for three centuries it resisted all attempts to break it this earned it the description le chiffre indéchiffrable (French for the indecipherable cipher ). Many people have tried to implement encryption schemes that are essentially Vigenère ciphers.[3] Friedrich Kasiski was the first to publish a general method of deciphering a Vigenère cipher.Unlike the monoalphabetic ciphers, polyalphabetic ciphers are not susceptible to frequency analysis, as more than one letter in the plaintext can be represented by a single letter in the encryption.

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[model] = SVMTRAIN(X, Y, C, kernelFunction, tol, max_passes) trains an SVM classifier and returns trained model. X is the matrix of training examples. Each row is a training example, and the jth column holds the jth feature. Y is a column matrix containing 1 for positive examples and 0 for negative examples. C is the standard SVM regularization parameter. tol is a tolerance value used for determining equality of floating point numbers. max_passes controls the number of iterations over the dataset (without changes to alpha) before the algorithm quits.

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lsm

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