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- =Rhea Section for Data Structure [[ECE608]] Professor Ghafoor, 2011= [[Category:Data StructureECE6082011Ghafoor]]263 B (34 words) - 10:41, 29 December 2010
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- **[[2011 Data Structure ECE608 Ghafoor|ECE608: Data Structure, Prof. Ghafoor]]13 KB (1,570 words) - 13:53, 7 August 2018
- ...dge, needs less data, is more modular, and can handle missing or corrupted data. Methods include mixture models and Hidden Markov Models. The latter approa ...rtitionings which form a taxonomy. Another possibility is to learn a graph structure between the clusters, as in the Growing Neural Gas. The quality of the clus31 KB (4,832 words) - 18:13, 22 October 2010
- Clustering is a nonlinear activity that groups data by generating ideas, images and chunks around a stimulus point. As clusteri ...gives a simplistic representation of clustering. Figure 1 has unclustered data initially, not necessarily grouped, and the application of a clustering alg8 KB (1,173 words) - 12:41, 26 April 2008
- By using union of hypersurfaces, classes can be separated by a tree based structure. Tree based separation is harder, and obtaining an optimum decision tree is '''Meaning 1:''' One that will make few mistakes when used to classify actual data. In statistical language: small probability of error.5 KB (737 words) - 08:45, 17 January 2013
- ...as a key to make SVM an effective tool for classifying linearly separable data. Here we see some examples of kernel functions, and the condition that det ...ance to hyperplane with same computational cost as training SVM in initial data space.8 KB (1,354 words) - 08:51, 17 January 2013
- ...ear discriminant is a classification method that projects high-dimensional data onto a line and performs classification in this one-dimensional space. The ...nknown image data to classes which minimize the distance between the image data and the class in multi-feature space. The distance is defined as an index o3 KB (430 words) - 10:40, 24 April 2008
- applications, such as data mining, web searching, retrieval of multimedia data, face recognition, and cursive handwriting recognition, the underlying process that generated the data. The contributions of this special issue cover a wide39 KB (5,715 words) - 10:52, 25 April 2008
- The purpose is to generate decision tree using the training data. The idea: Ask a series of simple questions following a tree structure (linear 1-D).6 KB (1,047 words) - 08:42, 17 January 2013
- If sample data is pure i.e. small variance in the training data can yield large variations in decision rules obtained.6 KB (806 words) - 08:42, 17 January 2013
- A machine learning technique in which a function is created from training data. ...output). To achieve this, the learner has to generalize from the presented data to unseen situations in a "reasonable" way.3 KB (454 words) - 09:09, 7 April 2008
- ...e model structure is not specified a priori but is instead determined from data. The term nonparametric is not meant to imply that such models completely l319 B (52 words) - 01:42, 17 April 2008
- *Each criterion imposes a certain structure on the data. *If the data conforms to this structure, the true clusters can be discovered.8 KB (1,244 words) - 08:44, 17 January 2013
- Spectral methods are widely used to reduce data dimensionality in order to enable a more effective use of several pattern r ...sitive definite kernel of dimension m, the extrinsic dimensionality of the data.2 KB (238 words) - 10:41, 28 April 2008
- By using union of hypersurfaces, classes can be separated by a tree based structure. Tree based separation is harder, and obtaining an optimum decision tree is '''Meaning 1:''' One that will make few mistakes when used to classify actual data. In statistical language: small probability of error.5 KB (744 words) - 11:17, 10 June 2013
- ...eal world phenomena. The success of the discipline in providing a rigorous structure on which principles of physics and chemistry can be scaffolded is perhaps m ...ime, or a dynamic system evolving under known constraints, and we feed the data into our mathematical machinery. Before our eyes the information is transfo27 KB (4,384 words) - 17:47, 26 October 2009
- *One of the way php decides what to do with user input data is through actions. ...L forms, radio buttons, checklists etc. all need a defined action when the data is submitted. The handling action may be a page, or another function within7 KB (1,129 words) - 06:32, 28 May 2010
- Given the training data, observation sequence(s) X, and basic model structure, (i.e. number of states, number of possible observations) how to learn the In order to learn an HMM model from given training data, by using forward and backward algorithm, and plugging them into an EM algo4 KB (710 words) - 18:50, 9 May 2010
- ...dge, needs less data, is more modular, and can handle missing or corrupted data. Methods include mixture models and Hidden Markov Models. The latter approa ...rtitionings which form a taxonomy. Another possibility is to learn a graph structure between the clusters, as in the Growing Neural Gas. The quality of the clus31 KB (4,787 words) - 18:21, 22 October 2010
- =Rhea Section for Data Structure [[ECE608]] Professor Ghafoor, 2011= [[Category:Data StructureECE6082011Ghafoor]]263 B (34 words) - 10:41, 29 December 2010
- ...e seriously. The ability of project design and ability of using MATLAB for data analysis are two basic skills for an engineer. So work hard on this course, ...es that I took, excel was never used, but I believe it is heavily used for data organization in the industry. The most important things to learn in the FYE11 KB (2,089 words) - 14:16, 16 December 2011
