BigDataFr recommends: An Extended classification and Comparison of NoSQL Big Data Models In last few years, the volume of the data has grown manyfold. The data storages have been inundated by various disparate potential data outlets, leading by social media such as Facebook, Twitter, etc. The existing data models are largely unable to illuminate the […]
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[arXiv] BigDataFr recommends: Learning to Hash for Indexing Big Data – A Survey
BigDataFr recommends: Learning to Hash for Indexing Big Data – A Survey ‘The explosive growth in big data has attracted much attention in designing efficient indexing and search methods recently. In many critical applications such as large-scale search and pattern matching, finding the nearest neighbors to a query is a fundamental research problem. However, the […]
[arxiv] BIgDataFr recommends: Train faster, generalize better – Stability of stochastic gradient descent #datascientist
BigDataFr recommends: Train faster, generalize better – Stability of stochastic gradient descent ‘We show that any model trained by a stochastic gradient method with few iterations has vanishing generalization error. We prove this by showing the method is algorithmically stable in the sense of Bousquet and Elisseeff. Our analysis only employs elementary tools from convex […]
[arXiv] BigDataFr recommends: Empirical Big Data Research- A Systematic Literature Mapping #machinelearning
BigDataFr recommends: Empirical Big Data Research- A Systematic Literature Mapping « Background: Big Data is a relatively new field of research and technology, and literature reports a wide variety of concepts labeled with Big Data. The maturity of a research field can be measured in the number of publications containing empirical results. In this paper we […]
[arXiv] BigDataFr recommends: Deep Broad Learning – Big Models for Big Data
BigDataFr recommends: Deep Broad Learning – Big Models for Big Data ‘Deep learning has demonstrated the power of detailed modeling of complex high-order (multivariate) interactions in data. For some learning tasks there is power in learning models that are not only Deep but also Broad. […] The most accurate models will integrate all that information. […]
[arXiv] BigDataFr recommends: Improving Big Data Visual Analytics with Interactive Virtual Reality #datascientist #machine learning
BigDataFr recommends: Improving Big Data Visual Analytics with Interactive Virtual Reality ‘For decades, the growth and volume of digital data collection has made it challenging to digest large volumes of information and extract underlying structure. Coined ‘Big Data’, massive amounts of information has quite often been gathered inconsistently (e.g from many sources, of various forms, […]
[arXiv] BigDataFr recommends:A Flexible Coordinate Descent Method for Big Data Applications
BigDataFr recommends: A Flexible Coordinate Descent Method for Big Data Applications ‘In this paper we present a novel randomized block coordinate descent method for the minimization of a convex composite objective function. The method uses (approximate) partial second-order (curvature) information, so that the algorithm performance is more robust when applied to highly nonseparable or ill […]
[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 […]