Search Results - subject:"Machine Learning"

Automated trading with boosting and expert weighting

Creamer, German; Freund, Yoav - In: Quantitative Finance 10 (2010) 4, pp. 401-420

We propose a multi-stock automated trading system that relies on a layered structure consisting of a machine learning …

Persistent link: https://www.econbiz.de/10008675039

An Empirical Comparison of Machine Learning Models for Time Series Forecasting

Ahmed, Nesreen; Atiya, Amir; Gayar, Neamat El; … - In: Econometric Reviews 29 (2010) 5-6, pp. 594-621

In this work we present a large scale comparison study for the major machine learning models for time series …). There have been very few, if any, large scale comparison studies for machine learning models for the regression or the time …

Persistent link: https://www.econbiz.de/10008691624

Predicting Baseball Hall of Fame Membership using a Radial Basis Function Network

Smith, Lloyd; Downey, James - In: Journal of Quantitative Analysis in Sports 5 (2009) 1, pp. 6-6

to build separate machine learning models for pitchers and non-pitchers. These models use simple player statistics such …

Persistent link: https://www.econbiz.de/10005459204

A machine learning approach for Arabic text classification using N-gram frequency statistics

Khreisat, Laila - In: Journal of Informetrics 3 (2009) 1, pp. 72-77

In this paper a machine learning approach for classifying Arabic text documents is presented. To handle the high …

Persistent link: https://www.econbiz.de/10010795076

Sentiment analysis: A combined approach

Prabowo, Rudy; Thelwall, Mike - In: Journal of Informetrics 3 (2009) 2, pp. 143-157

learning and machine learning into a new combined method. This method is tested on movie reviews, product reviews and MySpace …

Persistent link: https://www.econbiz.de/10010795158

Data-driven topo-climatic mapping with machine learning methods

Pozdnoukhov, A.; Foresti, L.; Kanevski, M. - In: Natural Hazards 50 (2009) 3, pp. 497-518

process large amounts of available data and produce predictions at fine spatial scales. These are the algorithms of machine … learning, which are aimed at non-parametric robust modelling of non-linear dependencies from empirical data. The computational …

Persistent link: https://www.econbiz.de/10010996123

Local decision making and decision fusion in hierarchical levels

Beldek, Ulaş; Leblebicioğlu, Kemal - In: TOP: An Official Journal of the Spanish Society of … 17 (2009) 1, pp. 44-69

Persistent link: https://www.econbiz.de/10004995461

Super Learner

Laan, Mark van der; Polley, Eric; Hubbard, Alan - In: Statistical Applications in Genetics and Molecular Biology 6 (2009) 1, pp. 25-25

When trying to learn a model for the prediction of an outcome given a set of covariates, a statistician has many estimation procedures in their toolbox. A few examples of these candidate learners are: least squares, least angle regression, random forests, and spline regression. Previous articles...

Persistent link: https://www.econbiz.de/10005046604

Super Learning: An Application to the Prediction of HIV-1 Drug Resistance

Sinisi, Sandra; Polley, Eric; Petersen, Maya; Rhee, Soo-Yon - In: Statistical Applications in Genetics and Molecular Biology 6 (2009) 1, pp. 7-7

Many alternative data-adaptive algorithms can be used to learn a predictor based on observed data. Examples of such learners include decision trees, neural networks, support vector regression, least angle regression, logic regression, and the Deletion/Substitution/Addition algorithm. The optimal...

Persistent link: https://www.econbiz.de/10005046616

Self-adaptive support vector machines: modelling and experiments

Du, Peng; Peng, Jiming; Terlaky, Tamás - In: Computational Management Science 6 (2009) 1, pp. 41-51

Persistent link: https://www.econbiz.de/10005147283