We propose a new learning algorithm based on locally weighted regression for feature labeling by end users, enabling them to point out which features are ...
Our results strongly suggest that allowing users to freely choose features to label is a promising method for allowing end users to improve learning algorithms.
To solve this prob- lem, we propose a new learning algorithm based on locally weighted regression for feature labeling by end users, ena- bling them to point ...
To solve this problem, we propose a new learning algorithm based on locally weighted regression for feature labeling by end users, enabling them to point out ...
We propose new supervised and semi-supervised learning algorithms based on locally-weighted logistic regression for feature labeling by end users.
Aug 4, 2011 · To solve this problem, we propose a new learning algorithm based on Locally Weighted Logistic Regression for feature labeling by end users, ...
Missing: approach. | Show results with:approach.
Feb 13, 2014 · To solve this problem, we propose new supervised and semi-supervised learning algorithms based on locally weighted logistic regression for ...
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What is a locally weighted regression?
End-user feature labeling: Supervised and semi-supervised approaches based on locally-weighted logistic regression · Shubhomoy Das, Travis Moore, Weng-Keen ...
Feb 9, 2017 · The most interesting part of locally weighted linear regression is that, the model changes when x changes (keep in mind x is the data point you want to query).
End-user feature labeling: Supervised and semi-supervised approaches based on locally-weighted logistic regression · Artificial Intelligence 204: 56-74 · 2013 ...