Optimization of k nearest neighbor density estimates

By Keinosuke Fukunaga, Larry D. Hostetler. 1973

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Citations in the corpus

Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges (2021, background): “Many variants of the original kNN classification scheme have been developed in the 1970s and 80s (Dudani, 1976; Fukunaga and Hostetler, 1973; Keller et al., 1985; Bezdek et al., 1986).”
Empirical estimation of entropy functionals with confidence (2010, unknown): “Also, we note that h(X) = h1(X) = c(X)f −2/d(X) [15], where c(X) := c1(X) = Γ (2/d)( 2 )tr[∇2(f(X))].”
Multivariate generalizations of the Wald--Wolfowitz and Smirnov two-sample tests (1979, background): “The considerations that lead to specific choices are the same as for any. other procedure involving interpoint distances (Fukunaga and Hostetler, 1973).”
A theoretical distribution analysis of synthetic minority oversampling technique (SMOTE) for imbalanced learning (2023, unknown): “Furthermore, according to the work of Fukunaga and Hostetler (1973), u follows a Beta distribution such that u ∼ Beta ( u ; k , N − k ) .”
An Algorithm for Finding Best Matches in Logarithmic Expected Time (1977, background): “I t can be shown [5] that the probability distribution of u~(Xq) follows a beta distribution B(m, N) ; that is,”
A weighted k-nearest neighbor density estimate for geometric inference (2011, methodology): “For the original Loftsgaarden and Quesenberry’s density estimate, the optimization problem of k n with respect to the mean square error criterion is thoroughly discussed in Fukunaga and Hostetler [25].”