k-Nearest neighbors: From global to local

Oren Anava, Kfir Y. Levy

פרסום מחקרי: פרסום בכתב עתמאמר מכנסביקורת עמיתים

תקציר

The weighted k-nearest neighbors algorithm is one of the most fundamental non-parametric methods in pattern recognition and machine learning. The question of setting the optimal number of neighbors as well as the optimal weights has received much attention throughout the years, nevertheless this problem seems to have remained unsettled. In this paper we offer a simple approach to locally weighted regression/classification, where we make the bias-variance tradeoff explicit. Our formulation enables us to phrase a notion of optimal weights, and to efficiently find these weights as well as the optimal number of neighbors efficiently and adaptively, for each data point whose value we wish to estimate. The applicability of our approach is demonstrated on several datasets, showing superior performance over standard locally weighted methods.

שפה מקוריתאנגלית
עמודים (מ-עד)4923-4931
מספר עמודים9
כתב עתAdvances in Neural Information Processing Systems
סטטוס פרסוםפורסם - 2016
פורסם באופן חיצוניכן
אירוע30th Annual Conference on Neural Information Processing Systems, NIPS 2016 - Barcelona, ספרד
משך הזמן: 5 דצמ׳ 201610 דצמ׳ 2016

ASJC Scopus subject areas

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