Bagging, Boosting and Ensemble Methods

TL;DR: It is demonstrated that Ensembles of deep L STM learners outperform individual LSTM networks and thus push the state-of-the-art in human activity recognition using wearables.

TL;DR: This study explores different textual properties that can be used to distinguish fake contents from real and trains a combination of different machine learning algorithms using various ensemble methods and evaluates their performance on 4 real world datasets.

TL;DR: In this article, the authors present a review of the use of machine learning to predict treatment outcomes in psychiatry, ranging from medications and psychotherapies to digital interventions and neurobiological treatments.

TL;DR: This system is capable of outperforming the Buy and Hold (B&H) strategy in three of the five analyzed financial markets, achieving an average rate of return of 49.26% in the portfolio, while the B&H achieves on average 32.41%.

TL;DR: A new method for estimation in linear models called the lasso, which minimizes the residual sum of squares subject to the sum of the absolute value of the coefficients being less than a constant, is proposed.

TL;DR: Presenting a method for determining the necessary and sufficient conditions for consistency of learning process, the author covers function estimates from small data pools, applying these estimations to real-life problems, and much more.

TL;DR: A general gradient descent boosting paradigm is developed for additive expansions based on any fitting criterion, and specific algorithms are presented for least-squares, least absolute deviation, and Huber-M loss functions for regression, and multiclass logistic likelihood for classification.