文件列表:

Tppcca-svmh\prtools3.1.7\@dataset\abs.m (50, 2002-09-18)
Tppcca-svmh\prtools3.1.7\@dataset\and.m (166, 2002-09-18)
Tppcca-svmh\prtools3.1.7\@dataset\classsizes.m (547, 2002-09-18)
Tppcca-svmh\prtools3.1.7\@dataset\conj.m (52, 2002-09-18)
Tppcca-svmh\prtools3.1.7\@dataset\ctranspose.m (59, 2002-09-18)
Tppcca-svmh\prtools3.1.7\@dataset\cumsum.m (104, 2002-09-18)
Tppcca-svmh\prtools3.1.7\@dataset\dataimage.m (91, 2002-09-18)
Tppcca-svmh\prtools3.1.7\@dataset\dataimsize.m (674, 2002-09-18)
Tppcca-svmh\prtools3.1.7\@dataset\dataset.asv (7538, 2009-02-26)
Tppcca-svmh\prtools3.1.7\@dataset\dataset.m (7538, 2002-09-18)
Tppcca-svmh\prtools3.1.7\@dataset\det.m (41, 2002-09-18)
Tppcca-svmh\prtools3.1.7\@dataset\display.m (293, 2002-09-18)
Tppcca-svmh\prtools3.1.7\@dataset\double.m (346, 2002-09-18)
Tppcca-svmh\prtools3.1.7\@dataset\eig.m (388, 2002-09-18)
Tppcca-svmh\prtools3.1.7\@dataset\end.m (139, 2002-09-18)
Tppcca-svmh\prtools3.1.7\@dataset\eq.m (198, 2002-09-18)
Tppcca-svmh\prtools3.1.7\@dataset\exp.m (50, 2002-09-18)
Tppcca-svmh\prtools3.1.7\@dataset\find.m (171, 2002-09-18)
Tppcca-svmh\prtools3.1.7\@dataset\finite.m (79, 2002-09-18)
Tppcca-svmh\prtools3.1.7\@dataset\ge.m (198, 2002-09-18)
Tppcca-svmh\prtools3.1.7\@dataset\getfeatd.m (163, 2002-09-18)
Tppcca-svmh\prtools3.1.7\@dataset\getimheight.m (327, 2002-09-18)
Tppcca-svmh\prtools3.1.7\@dataset\getlabd.m (240, 2002-09-18)
Tppcca-svmh\prtools3.1.7\@dataset\getlablist.m (334, 2002-09-18)
Tppcca-svmh\prtools3.1.7\@dataset\getlabn.m (382, 2002-09-18)
Tppcca-svmh\prtools3.1.7\@dataset\getprob.m (405, 2002-09-18)
Tppcca-svmh\prtools3.1.7\@dataset\gt.m (195, 2002-09-18)
Tppcca-svmh\prtools3.1.7\@dataset\hist.m (358, 2002-09-18)
Tppcca-svmh\prtools3.1.7\@dataset\horzcat.m (637, 2002-09-18)
Tppcca-svmh\prtools3.1.7\@dataset\image.m (1291, 2002-09-18)
Tppcca-svmh\prtools3.1.7\@dataset\imagesc.m (362, 2002-09-18)
Tppcca-svmh\prtools3.1.7\@dataset\inv.m (41, 2002-09-18)
Tppcca-svmh\prtools3.1.7\@dataset\invsig.m (69, 2002-09-18)
Tppcca-svmh\prtools3.1.7\@dataset\ldivide.m (440, 2002-09-18)
Tppcca-svmh\prtools3.1.7\@dataset\le.m (198, 2002-09-18)
Tppcca-svmh\prtools3.1.7\@dataset\length.m (77, 2002-09-18)
Tppcca-svmh\prtools3.1.7\@dataset\log.m (50, 2002-09-18)
Tppcca-svmh\prtools3.1.7\@dataset\lt.m (195, 2002-09-18)
Tppcca-svmh\prtools3.1.7\@dataset\max.m (613, 2002-09-18)
Tppcca-svmh\prtools3.1.7\@dataset\median.m (284, 2002-09-18)
... ...

% PRTools3.0 December 1999 % % PRTOOLS is a basic set of statistical pattern recognition % tools under Matlab. Not all commands are entirely tested. % % It is heavily upgraded from the previous version, November 1997 % % The main change is the use of "classes' and 'objects' offered % by Matlab-5 (Don't confuse them with the pattern recognition % classes and objects!). This simplifies the use, but may be % hard to understand for old users. Moreover, it makes PRTools3.0 % completely incompatible with old versions. % % There are two of these class-constructors: datasets and mappings, % the basic elements of pattern recognition. As use is made of % structures (entirely hidden for the user), they contain all types % of information that should be transferred otherwise explicitely % by the user: labels, sizes, weigths, mapping types, etcetera. % % What we can do now are defining datasets by % A = dataset(a,labels); % use it for training by defining a mapping by % W = ldc(A); % which may also be written as % W = A*ldc; % and use it for testing by: % e = A*W*testd; % This can be read as: map the dataset A on W (i.e. a space constructed % by class memberships) and test it (count the errors). % Mappings can be combined sequentially (W = W1*W2), by stacking % ([W = [W1 W2 W3]) and in parallel (W = [W1; W2; W3]). This facilitates % the construction of combined classifiers, e.q. W = maxc([W1 W2 W3]). % % A number of routines has been renamed, e.g.: % nlc : ldc % nqc : qdc % mlslc : fisherc % nmlc : nmc % Others are combined or have been moved outside the user sight into the % private directory. Many routines have been added. See the Contents file. % % PRTools3.1, January 2000 % % For affine mapping and normalizing mappings the multiplicative output % constant w.v is now integrated in the mapping coefficients. w.v is set % to one. % % Untrained mappings are no longer empty, so now isempty(fisherc) is 0. % These mappings can now be detected by the new routine 'istrained', so % istrained(fisherc) is 0. % % The display command for datasets has been changed such that it returns % the actual number of classes available in the dataset. % % New commands: % seldat: Select classes / features / objects from dataset % classim: Classify image using a given classifier % clevalf: Classifier evaluation (feature size curve) % cnormc: Renamed copy of normc % emclust: Expectation - Maximization Clustering % spatm: Augment image dataset with spatial label information % nlfisherm:Renamed copy of nlklm % % PRTools3.1.2, January 2001 % % The dataset structure changed in order to speed up the handling of labels. % This should not effect the calls as it is intended to be upwards compatible. % Newly created datasets, however, cannot be used under previous versions % of PRTools. % % The confmat command changed slightly to enable a more general use. As a % result the number of errors for the specific useage in which the two % labellists are identical is not returned anymore. % % A new command matchlab is added to rotate labels for optimal match. % % PRTools3.1.3, July 2001 % % New commands % pca: Principal component analysis (replaces overloaded procedure by klm) % maf: Maximum autocorrelation mapping, (pca for multi-band images) % % Consequently, some of the old, overloaded and confusing possiblities in klm % are removed. % % PRTools3.1.4, August 2001 % % some bugs are removed in @dataset/subsasgn.m and @dataset/dataset.m % all relating to the erroneous working of expressions like % A([1 2 3],:) = []; A(:,[1 2 3]); % if A is a dataset. % % kljlc: bug removed % gauss: label generation improved % gendats: labels changed from numeric to character % featsel* redundant output suppressed % % PRTools3.1.5, August 2001 % % knnc: Makes no use anymore of prior probabilities. % % New command % getprob: Retrieves dataset prior probabilities % % PRTools3.1.6, September 2001 % % normal_map: now generates scaled densities % qdc: now also in 2-class problems based on densities % plotm: internal scaling improved. Parameter added for % selecting contour. % scatterd: extended, a.o. with 3d plot % distm: now returns a dataset % gendat: relative class sizes supported % % New commands: % % subsc: Subspace Classifier % quadrc: Quadratic classifier (original 2-class qdc) % mclassc: Multi-class classifier by 2-class discriminants % classsizes: Returns sizes of classes in a dataset % getlablist: Returns label list of a dataset % See 'help datasets' for all means to retrieve data stored % in a dataset. % mds: Non-linear mapping by multi-dimensional scaling (Sammon) % classs: Multi-dimensional mapping by classical scaling % % ************************************************************* % % More information can be found by % help prtools % help datasets % help mappings % or in the manual (http://www.ph.tn.tudelft.nl/~bob/postscript/PRTools3.ps) % % This software can be used freely for inspection and academic research. % Please refer to % % R.P.W. Duin, PRTools 3, A Matlab Toolbox for Pattern Recognition, Delft % University of Technology, January 2000 % % if it has been useful to you. If you like to use the toolbox for commercial % purposes, please contact me. % % Bob Duin % % Copyright: R.P.W. Duin, duin@ph.tn.tudelft.nl % Faculty of Applied Physics, Delft University of Technology % P.O. Box 5046, 2600 GA Delft, The Netherlands

近期下载者：

相关文件：

评论：[我要评论] [举报此文件]

收藏者：