Nearest neighbor pattern classification

By Thomas M. Cover, P. Hart. 1967

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In Statistical Learning Theory

Citations in the corpus

Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges (2021, methodology): “A classic example of nearest neighbor-based techniques is k-nearest neighbors (kNN) (Fix and Hodges, 1951; Cover and Hart, 1967).”
A survey of data fusion in smart city applications (2019, methodology): “Common techniques for data association include Nearest Neighbors [59], Probabilistic Data Association [60], and Multiple Hypothesis Test [61].”
The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification (2014, methodology): “, nearest neighbors [10] or a supervised optimization-based method [5]).”
Deep Weighted Averaging Classifiers (2018, background): “stance directly to a vector of probabilities. There is, of course, a long history in machine learning of making predictions directly in terms of training instances, including nearest neighbor methods [9], kernel methods, including support vector machines [5, 8], and transductive learners more broadl”
EgoNet: identification of human disease ego-network modules (2014, methodology): “In this study, we selected three widely used methods: support vector machines (SVM) [26], K-nearest neighbors (KNN) [27] and random forests (RF) [28], and compared their performance for subnetwork identification through a simulation study.”
Natural Language Processing for EHR-Based Computational Phenotyping (2018, methodology): “Hybrid methods make use of more than one methods have also received increasing attention [138], [139], suggesting a promising direction for”
Does learning require memorization? a short tale about a long tail (2019, background): “For example, a classical work of Cover and Hart [CH+67] gives bounds on the generalization error of the 1-nearest neighbor algorithm.”
Efficient Private Statistics with Succinct Sketches (2015, methodology): “, pairs of videos watched by the same user) in order to perform recommendations based on K-Nearest Neighbor (KNN) algorithms [22]: even if only hundreds of programs are available, each user would have to encrypt and transmit a matrix of hundreds of thousands of values.”
A Survey on Metric Learning for Feature Vectors and Structured Data (2013, methodology): “…role in many machine learning, pattern recognition and data mining techniques.1 For instance, in classification, the k-Nearest Neighbor classifier (Cover and Hart, 1967) uses a metric to identify the nearest neighbors; many clustering algorithms, such as the prominent K-Means (Lloyd, 1982),…”
Does data interpolation contradict statistical optimality? (2018, background): “It is shown in (Cover and Hart, 1967) that the limit (as n tends to inﬁnity) of the classiﬁcation risk is no more than twice the Bayes risk.”
Computer Aided Diagnostic Support System for Skin Cancer: A Review of Techniques and Algorithms (2013, background): “e -nearest neighbour classi er [161, 162] is a nonparametricmethod of pattern recognition.”
Analysis of preprocessing vs. cost-sensitive learning for imbalanced classification. Open problems on intrinsic data characteristics (2012, methodology): “It must also be stressed that the positive or minority class is usually the one that has the highest interest from the learning point of view and it also implies a great cost when it is not well classified (Elkan, 2001).”
Machine learning for large-scale crop yield forecasting (2020, methodology): “…machine learning algorithms in predicting crop yield: (i) Ridge Regression (Hoerl and Kennard 1970), (ii) K -nearest Neighbors Regression (Cover and Hart 1967; Aha et al. 1991), (iii) Support Vector Machines Regression (Boser et al. 1992; Cortes and Vapnik 1995), and (iv) Gradient Boosted Decision…”
Bursty and Hierarchical Structure in Streams (2002, background): “[14]) to determine the conditional probability of a state sequence q = (q i : , .”
Choice of neighbor order in nearest-neighbor classification (2008, background): “Cover and Hart (1967) gave upper bounds for the limit of the risk of nearestneighbor classifiers.”