An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression

By Naomi S. Altman. 1992

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Citations in the corpus

Towards the Science of Security and Privacy in Machine Learning (2016, methodology): “1A few models are non-parametric: for instance the nearest neighbor [21].”
Black-Box vs. White-Box: Understanding Their Advantages and Weaknesses From a Practical Point of View (2019, background): “approach is the nearest neighbors-based model [21].”
SoK: Security and Privacy in Machine Learning (2018, neutral): “, nearest-neighbor [18]) are non-parametric.”
Making AI Forget You: Data Deletion in Machine Learning (2019, background): “One of the simplest examples of lazy learning is k -nearest neighbors [32, 4, 74], where deleting a point from the dataset at deletion time directly translates to an updated model at inference time.”
Analyzing ImageNet with Spectral Relevance Analysis: Towards ImageNet un-Hans'ed (2019, methodology): “As [7], we compute pair-wise (binary) aﬃnity scores a ij between all samples i, j using the k -nearest- neighbor (KNN) algorithm [29] on the previously computed distance measures and store them in a matrix A .”
A comparative analysis of kNN and decision tree methods for the Irish National Forest Inventory (2009, methodology): “In particular, the use of statistical pattern recognition methods, such as non-parametric k -Nearest Neighbour ( k NN) estimation (Fix and Hodges 1951 , Altman 1992 ), have been readily incorporated into satellite image based national forest inventory programmes (Tomppo 1991 , 1996, Tokola et a”
A3T-GCN: Attention Temporal Graph Convolutional Network for Traffic Forecasting (2020, methodology): “Common non-parametric models include k-nearest [2], support vector regression (SVR) [11, 30], fuzzy logic [34], Bayesian network[28], and neural network models.”
Portfolio Choice Problems (2010, background): “See Härdle (1990) or Altman (1992) for a more detailed discussion of kernel functions.”
Vokenization: Improving Language Understanding via Contextualized, Visually-Grounded Supervision (2020, methodology): “We adapt the idea of “nearest neighbor algorithm” (Altman, 1992) here.”
Finding Likely Errors with Bayesian Specifications (2017, methodology): “of Learning in Anomaly Detection To evaluate the role of learning, we compared with a traditional outlier detection method that does not require learning. k-nearest neighbor (k-NN) outlier detection (Altman 1992) uses a distance measure to compute the knearest neighbors of a given point within a dat”
Detection of COVID-19 Infection from Routine Blood Exams with Machine Learning: A Feasibility Study (2020, background): “– Decision Tree [35] (DT); – Extremely Randomized Trees [16] (ET); – K-nearest neighbors [2] (KNN); – Logistic Regression [20] (LR); – Näıve Bayes [23] (NB); – Random Forest [21] (RF); – Support Vector Machines [36] (SVM).”