Statistical Pattern Recognition

By R. Golden. 2001

2,160 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/138f8fc3e05509eb9d43d0446fcff21a73cf06ae

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In Statistics and Probability

Citations in the corpus

An Introductory Review of Deep Learning for Prediction Models With Big Data (2020, background): “Equation (3) deﬁnes how the output of a MLP is obtained from the input (Webb and Copsey, 2011).”
Recognizing Daily and Sports Activities in Two Open Source Machine Learning Environments Using Body-Worn Sensor Units (2014, methodology): “Scatter plots of the first five features selected by PCA are illustrated in Fig.”
Learning a Mahalanobis Metric from Equivalence Constraints (2005, methodology): “This family includes, among others, the techniques of Partial Least-Squares (PLS) (Geladi and Kowalski, 1986), Canonical Correlation Analysis (CCA) (Thompson, 1984) and Fisher’s Linear Discriminant (FLD) (Fukunaga, 1990).”
Adaptive Machine Learning for Credit Card Fraud Detection (2015, background): “We can define the risk of predicting an instance as positive or negative as follows: r = (1− p)l1,0 + pl1,1 r− = (1− p)l0,0 + pl0,1 Standard decision-making process based on decision theory [7, 33, 34] defines the optimal class of a sample as the one minimizing the risk (expected value of the loss f”
Proceedings of the 5th International Conference on Image and Video Retrieval (2006, neutral): “In order to determine the initial number of motion groups k , k-means is initialized with a leader initialization [12], on the translation between the ﬁrst two frames.”
Accurate Telemonitoring of Parkinson's Disease Progression by Noninvasive Speech Tests (2009, neutral): “well-known statistical resampling technique [35].”
Information-Geometric Dimensionality Reduction (2011, methodology): “This fact is critical when class PDFs are not linearly separable (e.g., such is the assumption of standard LDA).”
Calibrating Probability with Undersampling for Unbalanced Classification (2015, background): “machine learning (see for example [24], [3], [12]) defines the optimal class of a sample as the one minimizing the risk (expected value of the loss function).”
Application of interactive genetic algorithm to fashion design (2000, neutral): “Clustering refers to the process of grouping samples, which are similar within each group (Chen, 1973; Fukunaka, 1990; Gose et al., 1996) called cluster.”
Novelty detection: a review - part 1: statistical approaches (2003, background): “The distance measure itself can be Mahalanobis or some other probabilistic distance (Webb, 1999).”
HISTOGRAM-BASED SMOKE SEGMENTATION IN FOREST FIRE DETECTION SYSTEM (2009, neutral): “Alternative approach based on the assumption that the probability distribution at a continuity point can be estimated using the sample observation that falls within a region around that point utilizes the kernel density estimation technique [16], [17], [18].”
Classifier Technology and the Illusion of Progress (2006, background): “Key words and phrases: Supervised classification, error rate, misclassification rate, simplicity, principle of parsimony, population drift, selectivity bias, flat maximum effect, problem uncertainty, empirical comparisons.”
A Tutorial on the Cross-Entropy Method (2005, methodology): “(Webb, 1999), we wish to “quantize” or “encode” the vectors in Z in such a way that each vector is represented by one of K source vectors c1, .”
Using Spin Images for Efficient Object Recognition in Cluttered 3D Scenes (1999, neutral): “Furthermore, when vectors are projected into a subspace defined by the eigenvectors of largest eigenvalue, the l2 distance between projected vectors is the best approximation (with respect to mean square error) to the l2 distance between the unprojected vectors, given the dimension of the subspace [”