similarly good performance, with respect to the machine learning technique used, accentuating the importance of the regression …

Feature selection plays an important role in the successful application of machine learning techniques to large real … challenges for applying machine learning techniques to classify cognitive states based on fMRI data. In this study, we present an …

In machine learning problems a learning algorithm tries to learn the input–output dependency (relationship) of a system …

machine learning techniques to automatically estimate specific “characteristics” of a household from its electricity …

. Sensor-based forecasting utilizes machine learning techniques to infer the complex relationships between consumption and … used machine learning technique, and apply it to an empirical data-set from a multi-family residential building in New York …

appraisal environment in Malaysia. Machine learning (ML) techniques have been applied to real estate mass appraisal with varying …

In recent years the computers have shown to be a powerful tool in financial forecasting. Many machine learning … techniques have been utilized to predict movements in financial markets. Machine learning classifiers involve extending the past … investment. One of the main problems for any machine learning classifier is to deal with unbalanced classes. Specifically Genetic …

outperformed the parametric models in terms of prediction performance. Also artificial neural nets, a popular machine learning … universal function approximator. A recent successful machine learning method is boosting. Boosting is a relative new method to … discuss the Machine Learning techniques used in this paper. Generally, in Machine Learning a model is trained (fit, calibrated …