Sökning: "Lasso"

Visar resultat 1 - 5 av 28 avhandlingar innehållade ordet Lasso.

1. 1. Lasso Regularized Neural Networks

   Författare :Oskar Allerbo; Göteborgs universitet; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES;

   Sammanfattning : .... LÄS MER

3. 2. Parameter Estimation Using Sparse Modeling: Algorithms and Performance Analysis

   Författare :Ashkan Panahi; Chalmers tekniska högskola; []
   Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; compressed sensing; linear regression; spatial sparsity; convex variational optimization; DOA estimation; continuous LASSO; Complex LASSO;

   Sammanfattning : The idea of representing a signal in a classical computing machine has played a central role in the field of signal processing. The last two decades have witnessed an important breakthrough in this by taking all possible linear transforms and domains into account. LÄS MER

4. 3. Factor-Augmented Forecasting for High-Dimensional Data

   Författare :Ying Pang; Martin Sköld; Martin Singull; Stockholms universitet; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; High-Dimensional Data; Factor-Augmented Forecasting; Principal Component; Lasso; Cross Validation; Multi-Level Factor; Predictive Performance; matematisk statistik; Mathematical Statistics;

   Sammanfattning : In this thesis, we take a critical look at the factor-augmented forecast models, when a large number of time series variables available can provide the vital information for prediction. We discuss how to describe the commonality and idiosyncrasy of high-dimensional data by a handful of factors in various levels, and how to improve the predictive performance using these factors as augmented predictors. LÄS MER

5. 4. Exploring the Boundaries of Gene Regulatory Network Inference

   Författare :Andreas Tjärnberg; Erik Sonnhammer; Richard Bonneau; Stockholms universitet; []
   Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; GRN; gene regulatory network; network inference; signal to noise ratio; model selection; variable selection; data properties; reverse engineering; ordinary differential equations; gene networks; linear regression; lasso; Biochemistry towards Bioinformatics; biokemi med inriktning mot bioinformatik;

   Sammanfattning : To understand how the components of a complex system like the biological cell interact and regulate each other, we need to collect data for how the components respond to system perturbations. Such data can then be used to solve the inverse problem of inferring a network that describes how the pieces influence each other. LÄS MER

7. 5. Regularization for Sparseness and Smoothness : Applications in System Identification and Signal Processing

   Författare :Henrik Ohlsson; Lennart Ljung; Jacob Roll; Bo Wahlberg; Linköpings universitet; []
   Nyckelord :Regularization; sparsity; smothness; lasso; l1; fMRI; bio-feedback; TECHNOLOGY; TEKNIKVETENSKAP;

   Sammanfattning : In system identification, the Akaike Information Criterion (AIC) is a well known method to balance the model fit against model complexity. Regularization here acts as a price on model complexity. LÄS MER