1 / 3

The Idea in a Nutshell

The Idea in a Nutshell . Classify a new item based on the classes of its k nearest neighbors. Sensitivity to k distance metric how the k points contribute to the decision . Computational Challenges. Distance metric needs to be tuned (e.g., parameter scaling).

calvin
Télécharger la présentation

The Idea in a Nutshell

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. The Idea in a Nutshell • Classify a new item based on the classes of its k nearestneighbors. • Sensitivity to • k • distance metric • how the k points contribute to the decision

  2. Computational Challenges • Distance metric needs to be tuned (e.g., parameter scaling). • Given the neighbors, making a decision is tricky. • (Mixed) optimization. • Over-fitting should be avoided. • Finding the nearest neighbors might be time consuming. • Data management/data decomposition/ parallel processing.

  3. Simple Flexible Incremental Amenable to parallelization. Too simple Sensitive to the distance metric, k, and voting. Classification varies with the order data is provided. Pros Cons

More Related