Boosted Lasso
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Summary
The Boosted Lasso (BLasso) algorithm is proposed, which ties the Boosting algorithm with the Lasso method and is extended to minimizing a general convex loss penalized by ageneral convex function.
- Type
- report
- Published
- 2004-12-01
- Cited by
- 61
- References
- 31
- Access
- Open access
- OpenAlex
- https://openalex.org/W4231298827
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:9343539
Keywords
Lasso (programming language), Computer science, World Wide Web
References
- Special Invited Paper-Additive logistic regression: A statistical view of boosting
- JUST RELAX: CONVEX PROGRAMMING METHODS FOR SUBSET SELECTION AND SPARSE APPROXIMATION
- Greedy function approximation: A gradient boosting machine.
- Asymptotics for lasso-type estimators
- Model Selection and the Principle of Minimum Description Length
- The Elements of Statistical Learning: Data Mining, Inference, and Prediction
- On the LASSO and its Dual
- A new approach to variable selection in least squares problems
- Boosting a weak learning algorithm by majority
- Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
- A Statistical View of Some Chemometrics Regression Tools
- The Strength of Weak Learnability
- Stable recovery of sparse overcomplete representations in the presence of noise
- Penalized Regressions: The Bridge versus the Lasso
- For most large underdetermined systems of equations, the minimal 𝓁1‐norm near‐solution approximates the sparsest near‐solution
- Boosting as a Regularized Path to a Maximum Margin Classifier
- 1-norm Support Vector Machines
- Regression Shrinkage and Selection via the Lasso
- Boosting with early stopping: Convergence and consistency
- WAVESHRINK WITH FIRM SHRINKAGE
Cited by
- Efficient Mixed-Norm Regularization: Algorithms and Safe Screening Methods
- Statistical Methods for Evaluating Relational Structures in Multi-Dimensional Phenotypic Data for Neuropsychiatric Disorders
- Variable Selection Incorporating Prior Constraint Information into Lasso
- The Maximum Entropy Relaxation Path
- Essays on portfolio selection
- Multi-Task Feature Learning Via Efficient l2, 1-Norm Minimization
- The Sparse Laplacian Shrinkage Estimator for High-Dimensional Regression
- Online visual vocabulary pruning using pairwise constraints
- A Gradient-Based Optimization Algorithm for LASSO
- Embracing Statistical Challenges in the Information Technology Age
- Continuous Generalized Gradient Descent
- Boosting Algorithms: Regularization, Prediction and Model Fitting. Rejoinder.
- ADAPTIVE SEMI-VARYING COEFFICIENT MODEL SELECTION
- Unified LASSO Estimation by Least Squares Approximation
- On Early Stopping in Gradient Descent Learning
- Sparse Reconstruction by Separable Approximation
- Regularization Method for Predicting an Ordinal Response Using Longitudinal High-dimensional Genomic Data
- Shrinkage Estimation of the Varying Coefficient Model
- A graph-based elastic net for variable selection and module identification for genomic data analysis
- L1‐regularization path algorithm for generalized linear models
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