[arXiv] BigDataFr recommends: Scalable and Accurate Online Feature Selection for Big Data

BigDataFr recommends: Scalable and Accurate Online Feature Selection for Big Data Feature selection is important in many big data applications. There are at least two critical challenges. Firstly, in many applications, the dimensionality is extremely high, in millions, and keeps growing. Secondly, feature selection has to be highly scalable, preferably in an online manner such […]

[Datasciencecentral] BigDataFr recommends: Predictive Analytics in the Supply Chain

BigDataFr recommends: Predictive Analytics in the Supply Chain Predictive analytics are increasingly important to Supply Chain Management making the process more accurate, reliable, and at reduced cost. To be at the top of your game as a supply chain manager you need to understand and utilize advanced predictive analytics. As a large continuous process the […]

[arXiv] BigDataFr recommends: An Extended classification and Comparison of NoSQL Big Data Models

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 […]

[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: 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: Mapping Big Data into Knowledge Space with Cognitive Cyber-Infrastructure

BigDatFr recommends: Mapping Big Data into Knowledge Space with Cognitive Cyber-Infrastructure ‘Big data research has attracted great attention in science, technology, industry and society. It is developing with the evolving scientific paradigm, the fourth industrial revolution, and the transformational innovation of technologies. However, its nature and fundamental challenge have not been recognized, and its own […]

[arXiv] BigDataFr recommends: A Flexible Coordinate Descent Method for Big Data Applications #datascientist #machinelearning

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: Experimental Study of the Cloud Architecture Selection for Effective Big Data Processing

BigDataFr recommends: Experimental Study of the Cloud Architecture Selection for Effective Big Data Processing ‘Big data dictate their requirements to the hardware and software. Simple migration to the cloud data processing, while solving the problem of increasing computational capabilities, however creates some issues: the need to ensure the safety, the need to control the quality […]