功能為neighborhood components analysis，a quick matlab implementation of NCA (See Goldberger et al, NIPS04).
標簽： neighborhood components analysis
上傳時間： 2013-12-11
上傳用戶：tianjinfan
When I first studied Kalman filtering, I saw many advanced signal processing submissions here at the MATLAB Central File exchange, but I didn t See a heavily commented, basic Kalman filter present to allow someone new to Kalman filters to learn about creating them. So, a year later, I ve written a very simple, heavily commented discrete filter.
標簽： submissions processing filtering advanced
上傳時間： 2015-12-23
上傳用戶：變形金剛
ICP fit points in data to the points in model. Fit with respect to minimize the sum of square errors with the closest model points and data points. Ordinary usage: [R, T] = icp(model,data) INPUT: model - matrix with model points, data - matrix with data points, OUTPUT: R - rotation matrix and T - translation vector accordingly so newdata = R*data + T . newdata are transformed data points to fit model See help icp for more information
標簽： points the minimize respect
上傳時間： 2014-01-02
上傳用戶：gyq
The Stanford IBE library is a C implementation of the Boneh-Franklin identity-based encryption scheme. (See Boneh and Franklin, "Identity-Based Encryption from the Weil Pairing", CRYPTO 2001.) There are a few modifications and additions. The Boneh-Franklin scheme is used as a Key Encapsulation Mechanism, and off-the-shelf ciphers and HMACs are used for the actual encryption. (See Lynn, "Authenticated Identity-Based Encryption", available on eprint.
標簽： Boneh-Franklin implementation identity-based encryption
上傳時間： 2013-12-20
上傳用戶：yan2267246
proteus的例子－計算器，大家See一下
上傳時間： 2015-12-26
上傳用戶：gtzj
最大流，The programs are designed to run under BSD UNIX. All programs read from the standard input and write to the standard output. Run "make" to compile the programs and generators. File "list" lists the programs produced my "make". Input files are in DIMACS format. See sample.input.
標簽：
上傳時間： 2015-12-30
上傳用戶：zhenyushaw
Robotics TOOLBOX The Toolbox provides many functions that are useful in robotics including such things as kinematics, dynamics, and trajectory generation. The Toolbox is useful for simulation as well as analyzing results from experiments with real robots. Have fun with the toolbox. It is self-explanatory and very (robotics) user friendly...See the robot.pdf
標簽： functions including Robotics provides
上傳時間： 2014-12-06
上傳用戶：yyyyyyyyyy
This program is distributed in the hope that it will be useful, ** but WITHOUT ANY WARRANTY without even the implied warranty of ** MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the ** GNU General Public License for more details.
標簽： distributed WARRANTY program WITHOUT
上傳時間： 2016-01-11
上傳用戶：thesk123
These are the examples from the book Java Examples in a Nutshell, 2nd Edition, by David Flanagan. See the file index.html for more information.
標簽： the Examples Flanagan Nutshell
上傳時間： 2016-01-22
上傳用戶：13188549192
Boosting is a meta-learning approach that aims at combining an ensemble of weak classifiers to form a strong classifier. Adaptive Boosting (Adaboost) implements this idea as a greedy search for a linear combination of classifiers by overweighting the examples that are misclassified by each classifier. icsiboost implements Adaboost over stumps (one-level decision trees) on discrete and continuous attributes (words and real values). See http://en.wikipedia.org/wiki/AdaBoost and the papers by Y. Freund and R. Schapire for more details [1]. This approach is one of most efficient and simple to combine continuous and nominal values. Our implementation is aimed at allowing training from millions of examples by hundreds of features in a reasonable time/memory.
標簽： meta-learning classifiers combining Boosting
上傳時間： 2016-01-30
上傳用戶：songnanhua
