We propose Sparse Boosting (the SparseL2Boost algorithm), a variant on boosting with the squared error loss. SparseL2Boost yields sparser solutions than the ...

Our new SparseL2Boost algorithm achieves a higher degree of sparsity while still being computationally feasible, in contrast to all subset selection in linear ...

Jun 13, 2022 · We propose a framework for boosting that allows to enforce sparsity within and between groups. By using component-wise and group-wise gradient boosting.

Nov 19, 2017 · Numerical studies indicate that sparse boosting is effective in selecting important covariates and estimating the smooth coefficient functions.

Bin Yu. University of California, Berkeley. August 2005. Abstract. We propose Sparse Boosting (SparseL2Boost algorithm), a variant on boosting.

We propose a boosting algorithm that seeks to minimize the AdaBoost exponential loss of a composite classifier using only a sparse set of base classifiers.

Oct 24, 2023 · We introduce the information entropy of hidden state features into a pruning metric design, namely E-Sparse, to improve the accuracy of N:M sparsity on LLM.

Jan 10, 2024 · We propose Sparse Boosting (the SparseL2Boost algorithm), a variant on boosting with the squared error loss. SparseL2Boost yields sparser ...

A novel two-step sparse boosting approach is proposed to carry out the variable selection and the model-based prediction. As a new machine learning tool, ...

We propose Sparse Boosting (the SparseL2Boost algorithm), a variant on boosting with the squared error loss. SparseL2Boost yields sparser solutions than the ...