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www.eeworm.com/read/349711/3141426

changelog

ChangeLog file for zlib Changes in 1.1.4 (11 March 2002) - ZFREE was repeated on same allocation on some error conditions. This creates a security problem described in http://www.zlib.org/advi
www.eeworm.com/read/347989/3161545

svn-base cookbook.svn-base

MAGPIERSS RECIPES: Cooking with Corbies "Four and twenty blackbirds baked in a pie." 1. LIMIT THE NUMBER OF HEADLINES(AKA ITEMS) RETURNED. PROBLEM: You want to display the 10 (or 3) most
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java codematrix.java

package dragon.ir.classification.multiclass; /** * Code Matrix Interface * The code matrix handle the problem of how to build a set of binary classifiers for multi-class classification. A
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s labelbug.s

.code .align 4 ; A comment. This should not be interpreted as a label, but both of the ; following statements should. label_without_colon label_with_colon: ; A problem tege found... ; Input scrub
www.eeworm.com/read/347220/3173987

changelog

ChangeLog file for zlib Changes in 1.1.4 (11 March 2002) - ZFREE was repeated on same allocation on some error conditions. This creates a security problem described in http://www.zlib.org/advi
www.eeworm.com/read/344585/3207654

m demhmc3.m

%DEMHMC3 Demonstrate Bayesian regression with Hybrid Monte Carlo sampling. % % Description % The problem consists of one input variable X and one target variable % T with data generated by sampling X
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m demolgd1.m

%DEMOLGD1 Demonstrate simple MLP optimisation with on-line gradient descent % % Description % The problem consists of one input variable X and one target variable % T with data generated by sampling X
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m demgp.m

%DEMGP Demonstrate simple regression using a Gaussian Process. % % Description % The problem consists of one input variable X and one target variable % T. The values in X are chosen in two separated c
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m demhmc2.m

%DEMHMC2 Demonstrate Bayesian regression with Hybrid Monte Carlo sampling. % % Description % The problem consists of one input variable X and one target variable % T with data generated by sampling X
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m demrbf1.m

%DEMRBF1 Demonstrate simple regression using a radial basis function network. % % Description % The problem consists of one input variable X and one target variable % T with data generated by sampling