Pattern classification and scene analysis

By R. Duda, P. Hart. 1974

Subfields: Computer Vision and Pattern Recognition

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Citations in the corpus

Fast Edge Detection Using Structured Forests (2014, background): “The detection of edges is a critical preprocessing step for a variety of tasks, including object recognition [47], [17], segmentation [33], [1], and active contours [26].”
Minimum Error Rate Training in Statistical Machine Translation (2003, background): “Hence, under a so-called zero-one loss function this decision rule is optimal (Duda and Hart, 1973).”
Kernel Mean Embedding of Distributions: A Review and Beyonds (2016, methodology): “This algorithm is commonly known as a Parzen window classifier (Duda and Hart 1973).”
Pattern recognition (1974, neutral): “These attributes are not independent; all conﬁgurations of values are not possible and must respect the following constraints:”
APRIL: Active Preference-learning based Reinforcement Learning (2012, positive): “ε-means [9]) is used to define sensori-motor clusters.”
Kernel Methods for Relation Extraction (2002, methodology): “We evaluated three feature-based algorithms for learning linear discriminant functions: Naive-Bayes (Duda and Hart, 1973), Winnow (Littlestone, 1987), and SVM.”
Crisp Boundary Detection Using Pointwise Mutual Information (2014, methodology): “Classical examples are the Sobel [11], Roberts [12], Prewitt [13] and Canny [14] edge detectors, which all use local derivative ﬁlters of ﬁxed scale and only a few orientations.”
A comparison of line extraction algorithms using 2D laser rangefinder for indoor mobile robotics (2005, methodology): “In this case, the algorithm is named Iterative-End-Point-Fit [6], [18], [2], [23].”
Developments in Landsat Land Cover Classification Methods: A Review (2017, methodology): “Unsupervised classification was developed first through different clustering methods such as K-means and Interactive Self-Organization Data analysis (ISODATA) [7,50,51].”
Identification of high-level concept clones in source code (2001, methodology): “There are a variety of information retrieval methods including traditional [11] [37] approaches such as signature files, inversion, and clustering.”
An Outline of a Theory of Three-Way Decisions (2012, methodology): “Bayesian decision theory [3] can be applied to the derivation of DTRS as follows.”
The Curse of Highly Variable Functions for Local Kernel Machines (2005, methodology): “Since these methods share many characteristics with classical non-parametric statistical learning algorithms (such as the k-nearest neighbors and the Parzen windows regression and density estimation algorithms (Duda and Hart, 1973)), which have been shown to suffer from the so-called curse of dimens”
Unsupervised Learning of Models for Recognition (2000, methodology): “To t his end, we use a standardk-means clustering algorithm [7], which we tuned to produce a s t of about 100 patterns.”
Structuring Destination Image: A Qualitative Approach (2005, methodology): “A three-layer perceptron model using nearest neighbor analytical approaches (Duda and Hart 1973) has been adopted by tourism researchers (Tsaur, Chiu, and Huang 2002; Law 2001; Pattie and Snyder JOURNAL OF TRAVEL RESEARCH 145”
Naive (Bayes) at Forty: The Independence Assumption in Information Retrieval (1998, background): “When we know the P(ck Ix) exactly for a classification problem, classification can be done in an optimal way for a wide variety of effectiveness measures [10, 31].”