Adaptation Strategies for Automated Machine Learning on Evolving Data

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...

Strategies and Principles of Distributed Machine Learning on Big Data

The rise of Big Data has led to new demands for Machine Learning (ML) systems to learn complex models with millions to billions of parameters, that promise adequate capacity to digest massive datasets and offer powerful predictive analytics (such as high-dimensional latent features, intermediate representations, and decision functions) thereupon. In order to run ML algorithms at such scales, on...

Single-molecule Biophysics: Machine Learning for Automated Data Processing

Single-molecule fluorescence microscopy has been greatly successful in understanding biophysics at molecular level. This technique has been advancing toward higher throughput, which creates a need for a data-analysis tool to distinguish molecule of interest from other fluorescence signals. Here, we have used supervised machine-learning approaches to filter biological events of our interest, and...

Machine Learning for Automated Reasoning