[arXiv] BigDataFr recommends: Big Data Analytics in Bioinformatics – A Machine Learning Perspective #machine-learning

BigDataFr recommends: Big Data Analytics in Bioinformatics – A Machine Learning Perspective ‘Bioinformatics research is characterized by voluminous and incremental datasets and complex data analytics methods. The machine learning methods used in bioinformatics are iterative and parallel. These methods can be scaled to handle big data using the distributed and parallel computing technologies. Usually big […]

[arXiv] BigDataFr recommends: Predicting Regional Economic Indices using Big Data of Individual Bank Card Transactions #machine learning #datascientist

BigDataFr recommends: Predicting Regional Economic Indices using Big Data of Individual Bank Card Transactions ‘For centuries quality of life was a subject of studies across different disciplines. However, only with the emergence of a digital era, it became possible to investigate this topic on a larger scale. Over time it became clear that quality of […]

[arXiv] BigDataFr recommends: Behaviour of ABC for Big Data #datascientist #machinelearning

BigDataFr recommends: Behaviour of ABC for Big Data ‘Many statistical applications involve models that it is difficult to evaluate the likelihood, but relatively easy to sample from, which is called intractable likelihood. Approximate Bayesian computation (ABC) is a useful Monte Carlo method for inference of the unknown parameter in the intractable likelihood problem under Bayesian […]

[arXiv] BigDataFr recommends: Benchmarking Big Data Systems – State-of-the-Art and Future Directions #datascientist #machinelearning

BigDataFr recommends: Benchmarking Big Data Systems – State-of-the-Art and Future Directions ‘The great prosperity of big data systems such as Hadoop in recent years makes the benchmarking of these systems become crucial for both research and industry communities. The complexity, diversity, and rapid evolution of big data systems gives rise to various new challenges about […]