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Another Look at Distance-Weighted Discrimination

https://doi.org/10.1111/rssb.12244
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Distance‐weighted discrimination
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A note on margin-based loss functions in classification
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Neural Network Learning
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Convex Optimization
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Random Forests
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Support Vector Machines for Classification in Nonstandard Situations
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Support Vector Machines and the Bayes Rule in Classification
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Statistical Learning Theory
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Optimization Transfer Using Surrogate Objective Functions
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An Efficient Algorithm for Computing the HHSVM and Its Generalizations
inferredcites →https://doi.org/10.1080/10618600.2012.700878openalex-snapshot
Multiclass Distance-Weighted Discrimination
inferredcites →https://doi.org/10.1080/10618600.2015.1049700openalex-snapshot
Sparse Distance Weighted Discrimination
inferredcites →https://doi.org/10.1080/10618600.2017.1366915openalex-snapshot
Fast Algorithms for Large-Scale Generalized Distance Weighted Discrimination
inferredcites →https://doi.org/10.1093/bioinformatics/bts096openalex-snapshot
R/DWD: distance-weighted discrimination for classification, visualization and batch adjustment
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Geometric Representation of High Dimension, Low Sample Size Data
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Graph-based approach for airborne light detection and ranging segmentation
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Spline Models for Observational Data
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A Tutorial on MM Algorithms
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Convexity, Classification, and Risk Bounds
inferredcites →https://doi.org/10.1198/016214507000001120openalex-snapshot
Distance-Weighted Discrimination
inferredcites →https://doi.org/10.1198/106186005x25619openalex-snapshot
Kernel Logistic Regression and the Import Vector Machine
inferredcites →https://doi.org/10.1198/jasa.2010.tm08487openalex-snapshot
Weighted Distance Weighted Discrimination and Its Asymptotic Properties
inferredcites →https://doi.org/10.1198/jasa.2011.tm10319openalex-snapshot
Hard or Soft Classification? Large-Margin Unified Machines
inferredcites →https://doi.org/10.1198/jcgs.2010.09014openalex-snapshot
MM Algorithms for Some Discrete Multivariate Distributions
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Variable selection using MM algorithms
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The MM Alternative to EM
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Statistical behavior and consistency of classification methods based on convex risk minimization
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kernlab - An S4 Package for Kernel Methods in R
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gbm: Generalized Boosted Regression Models
inferredcites →https://doi.org/10.4310/sii.2015.v8.n3.a7openalex-snapshot
Distance-weighted Support Vector Machine
inferredcites →https://doi.org/10.48550/arxiv.1305.5029openalex-snapshot
Divide and Conquer Kernel Ridge Regression: A Distributed Algorithm with Minimax Optimal Rates
inferredcites →https://doi.org/10.48550/arxiv.1310.3004openalex-snapshot
Flexible High-dimensional Classification Machines and Their Asymptotic Properties
inferredcites →https://doi.org/10.48550/arxiv.1311.0914openalex-snapshot
A Divide-and-Conquer Solver for Kernel Support Vector Machines
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MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems
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Do we need hundreds of classifiers to solve real world classification problems
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Flexible high-dimensional classification machines and their asymptotic properties
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Advances in kernel methods: support vector learning
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kernlab - An S4 package for kernel methods in R
inferredcites →https://doi.org/10.7551/mitpress/1130.003.0007openalex-snapshot
Generalization Performance of Support Vector Machines and Other Pattern Classifiers
inferredcites →https://doi.org/10.7551/mitpress/1130.003.0009openalex-snapshot
Support Vector Machines, Reproducing Kernel Hilbert Spaces, and Randomized GACV
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Fast Training of Support Vector Machines Using Sequential Minimal Optimization

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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