• ...time trying to understand what is achieved by applying PCA on a particular data. Although my interpretation of how PCA work is prone to errors, I hope the ...the article by Jonathon Shlens's tutorial on PCA, inheriting his tutorial structure and examples. His article itself is a very readable and comprehensive one a
    6 KB (1,043 words) - 12:45, 3 March 2015
  • char *name;(if structure don't have pointer can write pointer Person P3 = P1; if copy attribute by a int data;
    2 KB (271 words) - 11:20, 28 February 2012
  • ...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.
    9 KB (1,389 words) - 11:19, 10 June 2013
  • 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).
    7 KB (1,082 words) - 11:23, 10 June 2013
  • If sample data is pure i.e. small variance in the training data can yield large variations in decision rules obtained.
    6 KB (837 words) - 11:23, 10 June 2013
  • Create maze structure containing the following * Structure's name NOUN w/ Capital Letter
    1 KB (232 words) - 05:24, 11 July 2012
  • char *name;(if structure don't have pointer can write pointer Person P3 = P1; if copy attribute by a int data;
    3 KB (506 words) - 05:25, 11 July 2012
  • *Each criterion imposes a certain structure on the data. *If the data conforms to this structure, the true clusters can be discovered.
    8 KB (1,214 words) - 11:24, 10 June 2013
  • int *data; array->data[lineNumber++] = strtol(buffer,NULL,10);
    3 KB (449 words) - 05:25, 11 July 2012
  • ...ic’s is very similar to the Edmond-Karp. By the way, with a special data structure which is called the Dynamic tree, we can improve the Dinic’s algorithm’
    15 KB (2,621 words) - 06:54, 21 March 2013
  • void intpart(int badget, int pos, int *data) data[pos]==i;
    7 KB (993 words) - 05:25, 11 July 2012
  • streaming data (next few lectures, focus on linklist only, and data type would just be integer)
    3 KB (488 words) - 09:58, 22 March 2012
  • c. grow and shrink based on run-time needs(dynamic structure) /*data*/
    10 KB (1,667 words) - 05:26, 11 July 2012
  • 3, three outcomes:1.file;2.structure;3.dynamic structure(linked list); Exam 2 covers: File I/O, Structure, Dynamic Structure,recursive
    6 KB (879 words) - 05:27, 11 July 2012
  • 2.structure 3.dynamic structure (linked list)
    17 KB (2,470 words) - 05:28, 11 July 2012
  • ...matted into CRSP's propreitary CRSPAccess database format, plus additional data tables that map the CRSP permanent company and security identifiers (PERMCO ...n major stock exchanges (NYSE, AMEX, NASDAQ) ||Covers basically accounting data for public, OTC and private companies
    11 KB (1,577 words) - 08:35, 23 April 2012
  • ...n I use radix sort to sort objects like floating point number whose binary structure is well defined? I defined a new data type called bitFloat as follows:
    7 KB (1,030 words) - 11:27, 18 March 2013
  • Anatomical imaging modalities only reveal the structure of an object, Figure 1, for example, compares MRI scans of two patients. Fu ...every <math>\theta</math> and <math>r</math>. Once you have collected your data, i.e the projections, you would want to form a three-dimensional representa
    8 KB (1,168 words) - 07:24, 26 February 2014
  • =Physical Design and Data Acquisition= ...the object being scanned. As the bed passes the rotating gantry, multiple data scans are collected and processed in real time. The path traced by the gant
    9 KB (1,390 words) - 07:24, 26 February 2014
  • ...ey emit a signal that can be decoded into a mapping of the body's internal structure. [[Image:intro_fig11.jpeg|400px|thumb|left|Fig 11: Effects of removing k-space data on a reconstructed phantom image. In A, image was obtained with full k-spac
    27 KB (4,777 words) - 07:25, 26 February 2014

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Abstract algebra continues the conceptual developments of linear algebra, on an even grander scale.

Dr. Paul Garrett