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...

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...

This paper briefly surveys existing methods for boosting multi-class classication algorithms, as well as compares the performance of one such implementation, Stagewise Additive Modeling using a Multi-class Exponential loss function (SAMME), against that of Softmax Regression, Classification and Regression Trees, and Neural Networks.

In this paper a novel and generic approach for model-based data clustering in a boosting framework is presented. This method uses the forward stagewise additive modeling to learn the base clustering models. The experimental results on relatively large scale datasets and also Caltech4 object recognition set demonstrate how the performance of relatively simple and computationally efficient base c...

Dr. Feldman’s paper constitutes a worthy continuation of the select pedigree of thorough neo-Piagetian theory constructions. It presents a general theory of stagewise cognitive development, in the spirit of Piaget’s naturalized epistemology. Theory construction at the general level at which Piaget operated has, for good reasons, an essential place in cognitive developmental psychology. In parti...

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...

Abstract: We consider the least angle regression and forward stagewise algorithms for solving penalized least squares regression problems. In Efron, Hastie, Johnstone & Tibshirani (2004) it is proved that the least angle regression algorithm, with a small modification, solves the lasso regression problem. Here we give an analogous result for incremental forward stagewise regression, showing tha...

Diierential Dynamic Programming (DDP) and stagewise Newton's method are both quadratically convergent algorithms for solving discrete time optimal control problem. Although these two algorithms share many theoretical similarities, they demonstrate signii-cantly diierent numerical performance. In this paper, we will compare and analyze these two algorithms in detail, and derive another quadratic...