Pattern classification, 2nd Edition

By R. Duda, P. Hart, David G. Stork. 2000

3,610 citations recorded by the source corpus.

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Citations in the corpus

Kernel Mean Shrinkage Estimators (2014, methodology): “One of the oldest and best-known classiﬁcation algorithms is the Parzen window classiﬁer (Duda et al., 2000).”
Online Continual Learning under Extreme Memory Constraints (2020, background): “This “mini-batch” based relaxation of the typical OL scenario [9] is commonly adopted in other COL settings [2].”
Propensity score estimation: machine learning and classification methods as alternatives to logistic regression (2010, methodology): “We will concentrate on neural networks, linear classifiers (in particular, support vector machines), decision trees, and meta-classifiers (specifically, boosting), each of which represents a different approach to the classification problem [10].”
Outside the Closed World: On Using Machine Learning for Network Intrusion Detection (2010, methodology): “(The idea here is similar to that employed in Principle Component Analysis, which aims to find which among a wide set of features contribute the most to particular clusters of activity [ 22 ].)”
Decision-level fusion in fingerprint verification (2001, methodology): “We use Parzen window density estimate to obtain the non-parametric distributions [ 8 ].”
Learning under Concept Drift: an Overview (2010, background): “[1 42 , 15 0, 72 , 7, 11 5] no (e ns em b.”
Unsupervised object class discovery via saliency-guided multiple class learning (2012, methodology): “top salient Sc windows in all the images, obtain K initial clusters using K-means[9], and sample the H K based on Pr(yi = 1, ki = k|xi; θ0) ∝ 1/(1 + exp(−σ||xi − ck||(2))) where ck is the centroid of the k cluster.”
Predicting postpartum changes in emotion and behavior via social media (2013, methodology): “…parametric and non-parametric classifiers to empirically determine the best suitable classification technique, including linear, quadratic, discriminant classifiers, naïve Bayes, k -nearest neighbor, decision trees, and Support Vector Machines with a radial-basis function (RBF) kernel [10].”
3D Object Modeling and Recognition Using Local Affine-Invariant Image Descriptors and Multi-View Spatial Constraints (2006, unknown): “Appearance-based methods—as originally proposed in the context of face recognition (Turk and Pentland, 1991; Pentland et al., 1994; Belhumeur et al., 1997) and 3D object recognition (Murase and Nayar, 1995; Selinger and Nelson, 1999)—take the opposite view, and prefer to explicit geometric reasoning”
Generalized Autoencoder: A Neural Network Framework for Dimensionality Reduction (2014, methodology): “In the following, we derive six implementations of the GAE inspired by previous dimensionality reduction ideas, namely PCA [10], LDA [2], ISOMAP [15], LLE [12], LE [1] and MFA [19].”
Anomaly detection: A survey (2009, methodology): “Classification [Tan et al. 2005; Duda et al. 2000] is used to learn a model (classifier) from a set of labeled data instances (training) and then, classify a test instance into”