document: Boosting with structure information in the functional space : an application to graph classification Boosting is a very successful classification algorithm that produces a linear combination of `` weak '' classifiers ( a.k.a. base learners ) to obtain high quality classification models . In this paper we propose a new boosting algorithm where base learners have structure relationships in the functional space . Though such relationships are generic , our work is particularly motivated by the emerging topic of pattern based classification for semi-structured data including graphs . Towards an efficient incorporation of the structure information , we have designed a general model where we use an undirected graph to capture the relationship of subgraph-based base learners . In our method , we combine both L1 norm and Laplacian based L2 norm penalty with Logit loss function of Logit Boost . In this approach , we enforce model sparsity and smoothness in the functional space spanned by the basis functions . We have derived efficient optimization algorithms based on coordinate decent for the new boosting formulation and theoretically prove that it exhibits a natural grouping effect for nearby spatial or overlapping features . Using comprehensive experimental study , we have demonstrated the effectiveness of the proposed learning methods .
doc_bio_tags: B O O O O O O O O O O O B I B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O