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- ...book is a very good reference for classification algorithms and clustering algorithms and analysis. The book's website contains very useful extra material, inclu654 B (88 words) - 00:12, 12 March 2008
- ===A paper explaining several neural-network algorithms including perceptron=== ...echnique) are described. The concept underlying these iterative adaptation algorithms is the minimal disturbance principle, which suggests that during training i39 KB (5,715 words) - 10:52, 25 April 2008
- 2. Machine Learning Algorithms for Surveillance and Event Detection6 KB (905 words) - 12:18, 28 April 2008
- ...algorithms focused on the detection of frontal human faces, whereas newer algorithms attempt to solve the more general and difficult problem of multiview face d2 KB (384 words) - 18:08, 16 March 2008
- Gradient descent algorithms are iterative methods for finding the minimum of a function in <math>\Re^n< ...and is itself a function that differs between the various gradient descent algorithms. Lastly, <math>\nabla f(x^{(k)})</math> is the [[gradient_Old Kiwi]] of the1 KB (201 words) - 10:46, 24 March 2008
- ...bpLbO4n0XjrgzhEuOD06Xfmo#PPA261,M1 Learning Kernel Classifiers, Theory and Algorithms By Ralf Herbrich]3 KB (446 words) - 06:36, 21 April 2013
- ...are used and a search must be made for optimal combining weights. Pruning algorithms can also be expensive since many candidate sub-trees must be formed and com ...cision trees do not treat well non-rectangular regions. Most decision-tree algorithms only examine a single field at a time. This leads to rectangular classifica2 KB (264 words) - 09:29, 10 April 2008
- ...h-theoretic framework has allowed for the development of general inference algorithms, which in many cases provide orders of magnitude speedups over brute-force709 B (100 words) - 17:44, 1 April 2008
- "Algorithms for clustering data," A.K. Jain, R.C. Dibes[http://www.cse.msu.edu/~jain/Cl Clustering algorithms can also be classified as follows:6 KB (806 words) - 08:42, 17 January 2013
- When we implement Hierarchical clustering algorithms we use an iterative procedure. Here is an example of how the so-called sing987 B (148 words) - 16:01, 6 April 2008
- ...thod of this kind is the one based on learning a mixture of Gaussians. The algorithms works in this way:967 B (155 words) - 16:22, 6 April 2008
- => Same result as "Single linkage algorithms"7 KB (1,060 words) - 08:43, 17 January 2013
- ==Agglomerate Algorithms for Hierarchical Clustering (from Distances)== [http://www.cs.man.ac.uk/~graham/cs2022/greedy/ Greedy and Minimum Spanning Algorithms]8 KB (1,254 words) - 08:43, 17 January 2013
- ...trees. A minimum spanning tree can be determined using Prim's or Kruskal's algorithms. Also see Prim's Algorithm and Kruskal's Algorithm.628 B (112 words) - 11:46, 11 April 2008
- =Graph Algorithms= When we want to implement graph algorithms on computer we can use one the 2 techniques:3 KB (557 words) - 17:49, 22 October 2010
- == Algorithms for clustering from feature vector ==8 KB (1,259 words) - 08:43, 17 January 2013
- == Various Clustering Algorithms == ...oblem, ideal solutions have exponential running time. For this reason, the algorithms presented by this group are sub-optimal, but run in polynomial time. [http:3 KB (585 words) - 14:39, 20 April 2008
- ...e used in practice, it represents an ideal classification rate which other algorithms may attempt to achieve.2 KB (399 words) - 14:03, 18 June 2008
- ...(or agglomerative) approach to clustering. Different from many clustering algorithms, this one uses a so-called "Rissanen criterion" or "minimum description len8 KB (1,244 words) - 08:44, 17 January 2013
- ...effective use of several pattern recognition techniques such as clustering algorithms. Here we review the most popular spectral methods.2 KB (238 words) - 10:41, 28 April 2008