A SPREADSHEET ALGORITHM FOR STAGEWISE SOLVENT EXTRACTION*

Stagewise Lasso Stagewise Lasso

Many statistical machine learning algorithms (in regression or classification) minimize either an empirical loss function as in AdaBoost, or a penalized empirical loss as in SVM. A single regularization tuning parameter controls the trade-off between fidelity to the data and generalibility, or equivalently between bias and variance. When this tuning parameter changes, a regularization “path” of...

Stagewise Modeling of Liquid-Liquid Extraction Column (RDC)

Stagewise forward mixing model considering coalescence and redispersion of drops was used to predict the performance of Rotating Disc Liquid-Liquid Extraction Contactors. Experimental data previously obtained in two RDC columns of 7.62cm diameter, 73.6cm height and 21.9cm diameter, 150cm height were used to evaluate the model predictions. Drop-side mass transfer coefficients were predicted appl...

A general framework for fast stagewise algorithms

Forward stagewise regression follows a very simple strategy for constructing a sequence of sparse regression estimates: it starts with all coefficients equal to zero, and iteratively updates the coefficient (by a small amount ) of the variable that achieves the maximal absolute inner product with the current residual. This procedure has an interesting connection to the lasso: under some conditi...

Training Support Vector Machines Using Greedy Stagewise Algorithm

Hard margin support vector machines (HM-SVMs) have a risk of getting overfitting in the presence of the noise. Soft margin SVMs deal with this problem by the introduction of the capacity control term and obtain the state of the art performance. However, this disposal leads to a relatively high computational cost. In this paper, an alternative method, greedy stagewise algorithm, named GS-SVMs is...

Magnetic solvent extraction