# 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