Regularization for sparsity in statistical analysis and machine learning
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- Type
- dissertation
- Published
- 2013-04-09
- Cited by
- 1
- References
- 249
- Access
- Open access
- OpenAlex
- https://openalex.org/W1671508429
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:129668048
Keywords
Regularization (linguistics), Statistical learning, Computer science, Machine learning, Citation
References
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- Graphical models: selecting causal and statistical models
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- Using Modified Lasso Regression to Learn Large Undirected Graphs in a Probabilistic Framework
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- Compression-Based Averaging of Selective Naive Bayes Classifiers
- Steps toward Artificial Intelligence
- The Dantzig selector: Statistical estimation when P is much larger than n
- A Distribution-Free Theory of Nonparametric Regression
- HMM and IOHMM modeling of EEG rhythms for asynchronous BCI systems
- Beyond Independence: Conditions for the Optimality of the Simple Bayesian Classifier
- Enumerating Markov Equivalence Classes of Acyclic Digraph Models
- Convergence of a Block Coordinate Descent Method for Nondifferentiable Minimization
- Fast factored density estimation and compression with bayesian networks
- Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning
- Learning Equivalence Classes of Bayesian Network Structures
- On the distribution of the adaptive LASSO estimator
- The doubly regularized support vector machine
- On characterizing Inclusion of Bayesian Networks
- Feature Selection via Concave Minimization and Support Vector Machines
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