Basic Pattern Definitions

  • Model: is anything that has specific descriptions in mathematical form.
  • Preprocessing: operation of simplifying subsequent operations without losing relative information.
  • Segmentation: operation of isolating image objects from background.
  • Feature space: space containing all pattern features.
  • Feature vector is a vector from feature space that contains the actual values of current model.
  • Feature Extraction: operation of reducing the data by measuring certain features or properties.
  • Training Samples: samples used to extract some information about domain of problem.
  • Decision Theory: theory of making a decision rule in order to minimize cost.
  • Decision Boundary: a boundary that distinguishes classes decision regions.
  • Novel Patterns: patterns that were not included in the training samples.
  • Generalization: classifier ability to classify novel patterns in right class.
  • Analysis by Synthesis: technique used to resolve problem of insufficient training data by having a model of how each pattern was produced.
  • Image Processing: techniques to process images for enhancements and other purposes.
  • Associative Memory: technique used to discover representative features of a group of patterns.
  • Regression: area of finding some functional description of data in order to predict new value.
  • Interpolation: area of inferring the function for intermediate ranges of input.
  • Density Estimation: the problem of estimating the density (probability) that a member of a certain category will be found to have particular features.
  • Pattern Recognition System:

  • Mereology: is the study of part/whole relationships in order to make the classifier categorize inputs as “make sense” rather than matching but not too much.
  • Invariant (distinguishing) Feature: features that are invariant to irrelevant transformations of the input
  • Occlusion: concept of hiding part of an object by other part
  • Feature Selection: operation of selecting most valuable features from a larger set of candidate features
  • Noise: any property of the sensed pattern which is not duo to the underlying model but instead to randomness in the world or the sensors
  • Error Rate: percentage of new patterns that are assigned to the wrong category
  • Risk (Conditional Risk): total expected cost
  • Evidence pooling, idea about having several classifiers used to categorize one sample. Here how can we resolve the problem of disagreeing classifying?
  • Multiple Classifier: multiple classifiers working on different aspects of the input
  • Design of Classification System:

  • Overfitting: situation where the classifier is tuned on training samples and is unable to classify novel pattern correctly.
  • Supervised Learning: learning technique where a teacher provides a category label or cost for each pattern in training set.
  • Unsupervised Learning (Clustering): learning technique where the system itself makes “natural groupings” of input patterns.
  • Reinforcement Learning: Calculating a tentative value for each category label to improve classification.


Chapter # 2

  • State of Nature: class that the current sample belongs to.
  • Prior: probability that the next sample will belongs to a specific class.
  • Decision Rule: rule that decides for which class the current sample belongs to.
  • Posterior: the probability of the state if nature being belongs to a specific class given that feature value x has been measured.
  • Evidence Factor: scaling factor that guarantees that the posterior probabilities sum to one
  • Loss Function: function that states how costly each action is, and is used to convert a probability determination into a decision.
  • Bayes Risk: minimum overall risk
  • Zero-One (symmetrical) Loss: loss function where each action is assigned to its category (i=j)
  • Decision Region: region for each class on the histogram
  • Dichotomizer: classifier that places a pattern in one of only two categories
  • Polychtomizer: classifier that places a pattern in more than two categories
  • Linear Discriminate Function: a linear function that is able to discriminate classes on the histogram
  • Template Matching: assigning x to the nearest mean

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