Heuristic Classifier Performance Bounds in High Dimensional Settings
نویسنده
چکیده
This paper is concerned with probability density estimation in high-dimensional settings. Simplified geometric arguments and supporting examples point to a performance bound which limits algorithm performance to that of either (1) nearest-neighbor or (2) single-kernel PDF estimators. A method of monitoring PDF estimation performance as well as recommendations for neural net and classification algorithm practitioners is provided.
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