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학술대회자료

Regularization based multi-output regression model tree

Multi-output regression has become an emerging problem in data-mining and machine learning. We propose a multi-output regression model tree to obtain both accuracy and interpretation. Each leaf of proposed model tree contains sparse linear models that exploit the relationship between response variables. We present an efficient splitting rule based on residual analysis. Experiments on several dataset identify the performance of the proposed model over benchmark methods.

1. Introduction

2. Method

3. Experiment

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