Statistical Pattern Recognition: A Review

By Anil K. Jain, Robert P. W. Duin, Jianchang Mao. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000

6,997 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/3626f388371b678b2f02f6eefc44fa5abc53ceb3

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In Computer Vision and Pattern Recognition

Citations in the corpus

Generalized feature extraction for structural pattern recognition in time-series data (2001, background): “See Friedman and Kandel [29], Fu [30], Jain [47], Nadler [65], and Young and Fu [91] for a discussion of these and other applications of pattern recognition.”
Hybrid intelligent techniques for MRI brain images classification (2010, methodology): “These methods provide suboptimal solution with a low computational cost and computational complexity [13].”
From BoW to CNN: Two Decades of Texture Representation for Texture Classification (2018, methodology): “As a classical pattern recognition problem, texture classiﬁcation primarily consists of two critical subproblems: texture representation and classiﬁcation [90].”
Searchlight analysis: Promise, pitfalls, and potential (2013, background): “…associated with whole-brain MVPA (the “curse” refers to computational difficulties which can occur when there are more voxels than examples, see (Clarke et al., 2008; Jain et al., 2000); it is minimized in searchlight analysis since relatively few voxels are typically included in each searchlight).”
Activity recognition using a single accelerometer placed at the wrist or ankle (2013, methodology): “Cross-validation is a well-established technique used in pattern recognition experiments to avoid training and testing on the same data when only small data sets are available (13).”
Automatic Classification for Pathological Prostate Images Based on Fractal Analysis (2009, background): “Not only the cost of recognition can be lowered down by reducing the number of features, but in some cases the performance of classification can be promoted by the optimal subset of features [50].”
Recent advances in the automatic recognition of audiovisual speech (2003, background): “The decision fusion framework, on the other hand, provides a mechanism for capturing these reliabilities, by borrowing from classifier combination theory, an active area of research with many applications [91] [92] [93].”
The distance function effect on k-nearest neighbor classification for medical datasets (2016, methodology): “Although using different k values are likely to produce different classification results, 1-NN is usually used as a benchmark for the other classifiers since it can provide reasonable classification performances in many pattern classification problems (Jain et al. 2000).”
Pattern classification with missing data: a review (2010, background): “In general, a pattern classification system is operated in two modes [5]: training (learning) and classification (testing).”
Three-dimensional computer-aided diagnosis scheme for detection of colonic polyps (2001, methodology): “Then, we employ afuzzy c-means algorithm[35] to remove polyp candidates due to noise.”
Image classification for content-based indexing (2001, methodology): “Can the classification be improved using feature subset selection methods? Selecting the optimal features is a problem of exponential time complexity and various suboptimal heuristics have been proposed [12], [13].”
On classification with bags, groups and sets (2014, neutral): “For a general introduction, refer to [19].”