Pattern recognition : a statistical approach

By P. Devijver, Josef Kittler. 1982

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Citations in the corpus

Transformation Invariance in Pattern Recognition-Tangent Distance and Tangent Propagation (1996, methodology): “Other methods like multiedit condensing (Devijver and Kittler, 1982; Voisin and Devijver, 1987) and K-d tree (Broder, 1990) are also possible.”
Neural networks for classification: a survey (2000, background): “Because of the curse of dimensionality [38], it is often necessary and beneficial to limit the number of input features in a classifier in order to have a good predictive and less computationally intensive model.”
Efficient mean-shift tracking via a new similarity measure (2005, background): “is a probabilistic distance, provided we have sufficient samples [11].”
Theory of classification : a survey of some recent advances (2005, background): “A partial list includes Fukunaga [97], Duda and Hart [77], Vapnik and Chervonenkis [233], Devijver and Kittler [70], Vapnik [229] [230], Breiman, Friedman, Olshen, and Stone [53], Natarajan [175], McLachlan [169], Anthony and Biggs [10], Kearns and Vazirani [117], Devroye, Györfi, and Lugosi [72], R”
Machine Learning Application in Reservoir Water Level Forecasting for Sustainable Hydropower Generation Strategy (2020, methodology): “Training and test partition or cross validation were the most popular ways for data partitioning [25,26].”
Modal transformation tools in structural dynamics and wind engineering (2000, methodology): “Applications of POD in data compaction and reduction and in pattern recognition and image processing are reported by Ahmed and Rao (1975) and Devijver and Kittler (1982) , respectively.”
Neural networks for pattern recognition (1995, background): “These algorithms can be generalized in various ways in order to allow small subsets of features which are collectively useful to be selected (Devijver and Kittler, 1982).”
The BANCA Database and Evaluation Protocol (2003, background): “In order to build reliable recognition and verification systems they need training and in general the larger the training set, the better the performance achieved [8].”
Intelligent Time-Adaptive Transient Stability Assessment System (2016, methodology): “n the input. The SBS algorithm employs a similar logic as SFS. The difference is that SBS starts with the measurements of all buses, and these buses are removed gradually during the selection process [36]. IEEE TRANSACTIONS ON POWER SYSTEMS TABLE VI TSA PERFORMANCE WITH NOISY PMU DATA t Training Set”
Consistency-based search in feature selection (2003, methodology): “From a statistics view point, five different measurements for class separability are analyzed in [14]: error probability, interclass distance, probabilistic distance, probabilistic dependence and entropy.”
Unsupervised Feature Selection Using Feature Similarity (2002, methodology): “However, PCA involves feature transformation and obtains a set of transformed features rather than a subset of the original features.”
Semantics-preserving dimensionality reduction: rough and fuzzy-rough-based approaches (2004, unknown): “Semantics-Preserving Dimensionality Reduction: Rough and Fuzzy-RoughBased Approaches.”