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  • CNSIPseminars
    ...signal processing strategies to achieve their excellent performance. These algorithms also incorporate characteristics of the human visual system (HVS), but typi
    5 KB (656 words) - 14:36, 4 May 2011
  • Insertion sort
    [[Category:Sorting algorithms]]
    2 KB (269 words) - 13:01, 14 January 2009
  • ECE608 (GhafoorSpring2009)
    ===Reviewed Algorithms===
    3 KB (441 words) - 17:45, 22 October 2010
  • Merge sort
    [[Category:Sorting algorithms]]
    2 KB (248 words) - 13:05, 14 January 2009
  • Heap sort
    [[Category:Sorting algorithms]]
    2 KB (357 words) - 20:51, 20 February 2009
  • Rhea's story
    ...ucation research to study these tools, and engineering research to develop algorithms and software to sustain them. You can learn more on the [https://engineerin
    5 KB (740 words) - 11:50, 5 October 2012
  • P = NP MA375Fall2008walther
    asymptotically faster algorithms may exist. in order to understand and analyze this question, academic backg
    5 KB (886 words) - 06:38, 21 March 2013
  • Ben Franklin's Magic Square MA453Fall2008walther
    [http://mathworld.wolfram.com/MagicSquare.html Magic Square explanation and algorithms]
    265 B (38 words) - 17:39, 29 October 2008
  • ECE662:Glossary Old Kiwi
    .... Some algorithms, like k-means, simply partition the feature space. Other algorithms, like single-link agglomeration, create nested partitionings which form a t ....dei.polimi.it/matteucc/Clustering/tutorial_html/ A tutorial on clustering algorithms]
    31 KB (4,832 words) - 18:13, 22 October 2010
  • Clustering Old Kiwi
    Some of the widely used algorithms for clustering include: * Survey of Clustering Algorithms, by Rui Xu and Donald Wunsch, IEEE Journal of Neural Networks, Vol 16, May
    8 KB (1,173 words) - 12:41, 26 April 2008
  • Homework 2 Old Kiwi
    ...box/ann/ Tool box holding a collection of Artificial Neural Networks (ANN) algorithms implemented for Matlab] : It contains lots of pattern recognition algorithms and gives the description and pesudo code of them.
    5 KB (746 words) - 16:33, 17 April 2008
  • Lecture 14 - ANNs, Non-parametric Density Estimation (Parzen Window) Old Kiwi
    ...nearest neighbor algorithm is amongst the simplest of all machine learning algorithms. An object is classified by a majority vote of its neighbors, with the obje ...ally when the size of the training set grows. Many nearest neighbor search algorithms have been proposed over the years; these generally seek to reduce the numbe
    13 KB (2,073 words) - 08:39, 17 January 2013
  • Textbooks Old Kiwi
    * A great reference if you are going to be developing pattern recognition algorithms * A great reference if you are going to be using the algorithms
    3 KB (340 words) - 17:58, 6 March 2008
  • Bayes Classification: Experiments and Notes Old Kiwi
    ...ollowing. We only have two classes, Class 1 and Class 2. You can test your algorithms for different priori probabilities for Class 1 and Class 2. However if you
    4 KB (735 words) - 22:49, 8 March 2008
  • ECE662:ChangeLog Old Kiwi
    * 2008/04/24 -- Added more notes on clustering algorithms * 2008/04/16 -- Created [[Lecture 25 - Clustering Algorithms_Old Kiwi]] (Algorithms for clustering from feature vector: All texts and equations 2-1 ~ 2-15)
    10 KB (1,418 words) - 12:21, 28 April 2008
  • What is Pattern Recognition Old Kiwi
    * Most algorithms do not scale well (example given of 2000 faces vs. 2 million faces in an ai * Most algorithms do well on the training data but fail miserably on real data.
    3 KB (436 words) - 08:43, 10 April 2008
  • Parametric Estimators Old Kiwi
    ...P equation is generally non-linear, and thus requires the use of iterative algorithms to compute a numerical solution as analytical expressions are not usually a
    6 KB (995 words) - 10:39, 20 May 2013
  • Tools Old Kiwi
    ...l uses artificial intelligence techniques like neural networks and genetic algorithms to find an optimal solution to classification problems. This description wa * Efficient Algorithms for K-Means [http://www.cs.umd.edu/~mount/Projects/KMeans]
    2 KB (307 words) - 17:13, 21 April 2008
  • "Pattern Classification" by Duda, Hart, and Stork Old Kiwi
    ...on pattern recognition. A great reference if you are going to be using the algorithms
    395 B (51 words) - 23:45, 11 March 2008
  • "Introduction to Statistical Pattern Recognition" by K. Fukunaga Old Kiwi
    ...e. A great reference if you are going to be developing pattern recognition algorithms
    917 B (96 words) - 05:06, 25 August 2010
  • "Introduction to Data Mining" by P-N Tan, M. Steinbach and V. Kumar Old Kiwi
    ...book is a very good reference for classification algorithms and clustering algorithms and analysis. The book's website contains very useful extra material, inclu
    654 B (88 words) - 00:12, 12 March 2008
  • Publications Old Kiwi
    ===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 i
    39 KB (5,715 words) - 10:52, 25 April 2008
  • Applications of Pattern Recognition Old Kiwi
    2. Machine Learning Algorithms for Surveillance and Event Detection
    6 KB (905 words) - 12:18, 28 April 2008
  • Face detection vs Face recognition Old Kiwi
    ...algorithms focused on the detection of frontal human faces, whereas newer algorithms attempt to solve the more general and difficult problem of multiview face d
    2 KB (384 words) - 18:08, 16 March 2008
  • Gradient Descent Old Kiwi
    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 the
    1 KB (201 words) - 10:46, 24 March 2008
  • Perceptron Convergence Theorem Old Kiwi
    ...bpLbO4n0XjrgzhEuOD06Xfmo#PPA261,M1 Learning Kernel Classifiers, Theory and Algorithms By Ralf Herbrich]
    3 KB (446 words) - 06:36, 21 April 2013
  • Decision Tree Old Kiwi
    ...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 classifica
    2 KB (264 words) - 09:29, 10 April 2008
  • Graphical Models Old Kiwi
    ...h-theoretic framework has allowed for the development of general inference algorithms, which in many cases provide orders of magnitude speedups over brute-force
    709 B (100 words) - 17:44, 1 April 2008
  • Lecture 22 - Decision Trees and Clustering Old Kiwi
    "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
  • Hierarchical clustering Old Kiwi
    When we implement Hierarchical clustering algorithms we use an iterative procedure. Here is an example of how the so-called sing
    987 B (148 words) - 16:01, 6 April 2008
  • Mixture of Gaussians Old Kiwi
    ...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
  • Lecture 23 - Spanning Trees Old Kiwi
    => Same result as "Single linkage algorithms"
    7 KB (1,060 words) - 08:43, 17 January 2013
  • Lecture 24 - Clustering and Hierarchical Clustering Old Kiwi
    ==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
  • Minimum Spanning Trees Old Kiwi
    ...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 Old Kiwi
    =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
  • Lecture 25 - Clustering Algorithms Old Kiwi
    == Algorithms for clustering from feature vector ==
    8 KB (1,259 words) - 08:43, 17 January 2013
  • Clustering Algorithms Old Kiwi
    == 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
  • Bayes classification Old Kiwi
    ...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
  • Lecture 26 - Statistical Clustering Methods Old Kiwi
    ...(or agglomerative) approach to clustering. Different from many clustering algorithms, this one uses a so-called "Rissanen criterion" or "minimum description len
    8 KB (1,244 words) - 08:44, 17 January 2013
  • Spectral Methods Old Kiwi
    ...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
  • Clustering Sequences Old Kiwi
    .... Some algorithms, like k-means, simply partition the feature space. Other algorithms, like single-link agglomeration, create nested partitionings which form a t
    781 B (110 words) - 08:32, 24 April 2008
  • Neural Networks using Genetic Algorithms Old Kiwi
    ===Using Genetic Algorithms in Computer Learning:=== Genetic Algorithms (GA) (http://en.wikipedia.org/wiki/Genetic_algorithm) are a method of deter
    2 KB (288 words) - 11:51, 25 April 2008
  • ECE662:BoutinSpring08 OldKiwi
    * [[Lecture 25 - Clustering Algorithms_OldKiwi|Lecture 25 - Clustering Algorithms]] * [[Learning algorithms_OldKiwi|Learning algorithms]] (blank in old QE)
    7 KB (875 words) - 07:11, 13 February 2012
  • Peer Legacy ECE495VIP
    ...it [[ECE495VIP|ECE495 - VIP]] this semester and was working on improvising algorithms to program on Graphic Processing Units (GPGPU team). Anybody interesting in
    5 KB (879 words) - 04:52, 13 December 2010
  • Super Tetris
    ...ent a one-step look-ahead and zero-step look-ahead Tetris algorithm. These algorithms were designed for normal Tetris, but the same ideas could be translated int [[Media:TetrisPresentation.ppt | Presentation over algorithms for Tetris AI]] (Unfortunately the videos do not work)
    813 B (126 words) - 16:48, 14 November 2013
  • Lossy versus Lossless Images
    ...as '''''lossless'''''. This means that with the aforementioned compression algorithms, the original signal can be faithfully ''reconstructed exactly, bit-by-bit' ...aq/part1/]). (This would reduce the 30 MB file down to about 15 MB.) These algorithms seek to reduce redundancies in the data by representing repeated data with
    6 KB (914 words) - 12:07, 22 October 2009
  • Homework3ECE438JPEG
    ...y zeroes. Once the numbers in the matrix are re-arranged, various encoding algorithms (including Huffman encoding which uses common trends in the DC values to pr
    5 KB (850 words) - 09:00, 23 September 2009
  • Student summary spectral analysis 2D signalsb
    ...mes also "Lenna"), is a famous image utilized in standard image processing algorithms and testing. The background behind this image is quite interesting. Here ar
    8 KB (1,397 words) - 11:23, 18 March 2013
  • ECE495 GPGPU Fall 09
    *<B>Data Analysis algorithms</B>
    592 B (78 words) - 12:37, 30 November 2009
  • Peer Legacy ECE438
    ...hen in lab we would apply them to real digital images in MATLAB by writing algorithms that would modify the images pixel by pixel. Very cool! --[[User:cpfeiffe|
    17 KB (3,004 words) - 08:11, 15 December 2011

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