Boosting Density Estimation
作者： Saharon Rosset , Eran Segal
DOI:
关键词: Gradient boosting 、 Machine learning 、 Algorithm 、 Unsupervised learning 、 Gradient descent 、 Learnability 、 Mathematics 、 Boosting (machine learning) 、 Function space 、 Bayesian network 、 Density estimation 、 Artificial intelligence
摘要: Several authors have suggested viewing boosting as a gradient descent search for good fit in function space. We apply gradient-based methodology to the unsupervised learning problem of density estimation. show convergence properties algorithm and prove that strength weak learnability property applies this well. illustrate potential approach through experiments with Bayesian networks learn models.
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