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  • G06F18/00
    		•  Pattern recognition [2023.01] HB CC 5B278
  • G06F18/10
    		•  . Pre-processing; Data cleansing [2023.01] HB CC 5B278
  • G06F18/15
    		•  .. Statistical pre-processing, e.g. techniques for normalisation or restoring missing data [2023.01] HB CC 5B278
  • G06F18/20
    		•  . Analysing [2023.01] HB CC 5B278
  • G06F18/21
    		•  ..Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation [2023.01] HB CC 5B278
  • G06F18/211
    		•  ...Selection of the most significant subset of features [2023.01] HB CC 5B278
  • G06F18/2111
    		•  .... by using evolutionary computational techniques, e.g. genetic algorithms [2023.01] HB CC 5B278
  • G06F18/2113
    		•  .... by ranking or filtering the set of features, e.g. using a measure of variance or of feature cross-correlation [2023.01] HB CC 5B278
  • G06F18/2115
    		•  .... by evaluating different subsets according to an optimisation criterion, e.g. class separability, forward selection or backward elimination [2023.01] HB CC 5B278
  • G06F18/213
    		•  ...Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods [2023.01] HB CC 5B278
  • G06F18/2131
    		•  .... based on a transform domain processing, e.g. wavelet transform [2023.01] HB CC 5B278
  • G06F18/2132
    		•  .... based on discrimination criteria, e.g. discriminant analysis [2023.01] HB CC 5B278
  • G06F18/2133
    		•  .... based on naturality criteria, e.g. with nonnegative factorisation or negative correlation [2023.01] HB CC 5B278
  • G06F18/2134
    		•  .... based on separation criteria, e.g. independent component analysis [2023.01] HB CC 5B278
  • G06F18/2135
    		•  .... based on approximation criteria, e.g. principal component analysis [2023.01] HB CC 5B278
  • G06F18/2136
    		•  .... based on sparsity criteria, e.g. with an overcomplete basis [2023.01] HB CC 5B278
  • G06F18/2137
    		•  .... based on criteria of topology preservation, e.g. multidimensional scaling or selforganising maps [2023.01] HB CC 5B278
  • G06F18/214
    		•  ...Generating training patterns; Bootstrap methods, e.g. bagging or boosting [2023.01] HB CC 5B278
  • G06F18/22
    		•  ..Matching criteria, e.g. proximity measures [2023.01] HB CC 5B278
  • G06F18/23
    		•  ..Clustering techniques [2023.01] HB CC 5B278
  • G06F18/231
    		•  ... Hierarchical techniques, i.e. dividing or merging pattern sets so as to obtain a dendrogram [2023.01] HB CC 5B278
  • G06F18/232
    		•  ... Non-hierarchical techniques [2023.01] HB CC 5B278
  • G06F18/2321
    		•  .... using statistics or function optimisation, e.g. modelling of probability density functions [2023.01] HB CC 5B278
  • G06F18/23211
    		•  ..... with adaptive number of clusters [2023.01] HB CC 5B278
  • G06F18/23213
    		•  ..... with fixed number of clusters, e.g. Kmeans clustering [2023.01] HB CC 5B278
  • G06F18/2323
    		•  .... based on graph theory, e.g. minimum spanning trees [MST] or graph cuts [2023.01] HB CC 5B278
  • G06F18/2325
    		•  .... using vector quantisation [2023.01] HB CC 5B278
  • G06F18/2337
    		•  .... using fuzzy logic, i.e. fuzzy clustering [2023.01] HB CC 5B278
  • G06F18/24
    		•  ..Classification techniques [2023.01] HB CC 5B278
  • G06F18/241
    		•  ... relating to the classification model, e.g. parametric or non-parametric approaches [2023.01] HB CC 5B278
  • G06F18/2411
    		•  .... based on the proximity to a decision surface,e.g. support vector machines [2023.01] HB CC 5B278
  • G06F18/2413
    		•  .... based on distances to training or reference HB CC 5B278
  • G06F18/2415
    		•  .... based on parametric or probabilistic models,e.g. based on likelihood ratio or false acceptance rate versus a false rejection rate [2023.01] HB CC 5B278
  • G06F18/243
    		•  ... relating to the number of classes [2023.01] HB CC 5B278
  • G06F18/2431
    		•  .... Multiple classes [2023.01] HB CC 5B278
  • G06F18/2433
    		•  .... Single-class perspective, e.g. one-against-all classification; Novelty detection; Outlier detection [2023.01] HB CC 5B278
  • G06F18/245
    		•  ... relating to the decision surface [2023.01] HB CC 5B278
  • G06F18/2451
    		•  .... linear, e.g. hyperplane [2023.01] HB CC 5B278
  • G06F18/2453
    		•  .... non-linear, e.g. polynomial classifier [2023.01] HB CC 5B278
  • G06F18/25
    		•  .. Fusion techniques [2023.01] HB CC 5B278
  • G06F18/26
    		•  .. Discovering frequent patterns [2023.01] HB CC 5B278
  • G06F18/27
    		•  .. Regression, e.g. linear or logistic regression [2023.01] HB CC 5B278
  • G06F18/28
    		•  .. Determining representative reference patterns, e.g.by averaging or distorting; Generating dictionaries [2023.01] HB CC 5B278
  • G06F18/30
    		•  .Post-processing [2023.01] HB CC 5B278
  • G06F18/40
    		•  .Software arrangements specially adapted for pattern recognition, e.g. user interfaces or toolboxes therefor [2023.01] HB CC 5B278
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