Automated Hyper-parameter Tuning for Machine Learning Models in Machine Health Prognostics

apsis - Framework for Automated Optimization of Machine Learning Hyper Parameters

Machine learning and the algorithms used for it have become more and more complex in the past years. Especially the growth of Deep Learning architectures has resulted in a large number of hyperparameters such as the number of hidden layers or the transfer function in a neural network which have to be tuned to achieve the best possible performance. Since the result of a hyperparameter choice can...

Machine Learning Models for Housing Prices Forecasting using Registration Data

This article has been compiled to identify the best model of housing price forecasting using machine learning methods with maximum accuracy and minimum error. Five important machine learning algorithms are used to predict housing prices, including Nearest Neighbor Regression Algorithm (KNNR), Support Vector Regression Algorithm (SVR), Random Forest Regression Algorithm (RFR), Extreme Gradient B...

Automated Machine Learning on Big Data using Stochastic Algorithm Tuning

We introduce a means of automating machine learning (ML) for big data tasks, by performing scalable stochastic Bayesian optimisation of ML algorithm parameters and hyper-parameters. More often than not, the critical tuning of ML algorithm parameters has relied on domain expertise from experts, along with laborious handtuning, brute search or lengthy sampling runs. Against this background, Bayes...

Machine Learning for Automated Reasoning

Using Paraphrases for Parameter Tuning in Statistical Machine Translation

Most state-of-the-art statistical machine translation systems use log-linear models, which are defined in terms of hypothesis features and weights for those features. It is standard to tune the feature weights in order to maximize a translation quality metric, using held-out test sentences and their corresponding reference translations. However, obtaining reference translations is expensive. In...