Discriminant Analysis and Statistical Pattern Recognition

By Theofanis Sapatinas. 2005

587 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/32d53d2d7f73c160ac9c8b45596fe78cfab8994a

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In Analytical Chemistry

In Artificial Intelligence

In Computer Vision and Pattern Recognition

Citations in the corpus

A comparative assessment between linear and quadratic discriminant analyses (LDA-QDA) with frequency ratio and weights-of-evidence models for forest fire susceptibility mapping in China (2017, methodology): “Quadratic discriminant analysis (QDA) is a univariate statistical method that is used for constructing amodel based on groups that consider the observed effective factors (McLachlan 2004; Seber 2009).”
Assessment of Various Fuzzy c-Mean Clustering Validation Indices for Mapping Mineral Prospectivity: Combination of Multifractal Geochemical Model and Mineralization Processes (2019, methodology): “Algorithms such as K-means (Clare and Cohen 2001; Ghezelbash et al. 2019c), fuzzy c-means (FCM) (Bezdek 1981), discriminant analysis (McLachlan 2004), hierarchical clustering (Templ et al. 2008) and self-organizing maps (Kohonen 1997) arrange a set of multi-attribute samples into coherent and…”
A meta-learning approach to automatic kernel selection for support vector machines (2006, unknown): “Support vector machines (SVMs) [28,6,5] are one of the most popular kernel-based learning algorithms, first introduced by Vapnik and his group in the mid-1990s.”
Recovering Petaflops in Contrastive Semi-Supervised Learning of Visual Representations (2020, methodology): “There is also the recent work [30], which makes use of EM [32] and clustering algorithms for estimating cluster prototypes [41].”
Robust Multivariate Analysis (2017, background): “Huberty and Olejnik (2006) and McLachlan (2004) are useful references for discriminant analysis. Silverman (1986, ∮ 6.1) and Raveh (1989) are good references for nonparametric discriminant analysis. Discrimination when p > n is interesting. See Cai and Liu (2011) and Mai, Zou and Yuan (2012). Logist”
The Dipping Phenomenon (2012, background): “consider classical quadratic discriminant analysis (QDA, [10]).”
9 Entropy Regularization (methodology): “In the probabilistic framework, semi-supervised induction is a missing data problem, which can be addressed by generative methods such as mixture models thanks to the EM algorithm and extensions thereof [McLachlan, 1992].”