BigDataFr recommends: Representation of Numbers with Incredibly Fast Converging Fractions […] Here we discuss a new system to represent numbers, for instance constants such as Pi, e, or log 2, using rational fractions. Each iteration doubles the precision (the number of correct decimals computed) making it converging much faster than current systems such as continued […]
Author: Big Data
[arXiv] BigDataFr recommends: Amplifying Inter-message Distance: On Information Divergence Measures in Big Data
BigDataFr recommends: Amplifying Inter-message Distance: On Information Divergence Measures in Big Data […] Subjects: Information Theory (cs.IT) Message identification (M-I) divergence is an important measure of the information distance between probability distributions, similar to Kullback-Leibler (K-L) and Renyi divergence. In fact, M-I divergence with a variable parameter can make an effect on characterization of distinction […]
[arXiv] BigDataFr recommends: Visualization of Big Spatial Data using Coresets for Kernel Density Estimates
BigDataFr recommends: Visualization of Big Spatial Data using Coresets for Kernel Density Estimates […] Subjects: Human-Computer Interaction (cs.HC); Computational Geometry (cs.CG) The size of large, geo-located datasets has reached scales where visualization of all data points is inefficient. Random sampling is a method to reduce the size of a dataset, yet it can introduce unwanted […]
[Datasciencecentral] BigDataFr recommends: Machine Learning as a Service – MLaaS
BigDataFr recommends: Machine Learning as a Service – MLaaS […] MLaaS is neither new nor rocket science or an unknown service. In today’s time there are hundreds of companies in this domain which are working as a service provider of MLaaS (SPMLaaS). Machine learning is into so many services and applications as on date and […]
[arXiv] BigDataFr recommends: A European research roadmap for optimizing societal impact of big data on environment and energy efficiency
BigDataFr recommends: A European research roadmap for optimizing societal impact of big data on environment and energy efficiency […] We present a roadmap to guide European research efforts towards a socially responsible big data economy that maximizes the positive impact of big data in environment and energy efficiency. The goal of the roadmap is to […]
[Datasciencecentral] BigDataFr recommends: Reinforcement Learning and AI
BigDataFr recommends: Reinforcement Learning and AI […] If you poled a group of data scientist just a few years back about how many machine learning problem types there are you would almost certainly have gotten a binary response: problem types were clearly divided into supervised and unsupervised. Supervised: You’ve got labeled data (clearly defined examples). […]
[arXiv] BigDataFr recommends: Massively-Parallel Feature Selection for Big Data
BigDataFr recommends: Massively-Parallel Feature Selection for Big Data […] We present the Parallel, Forward-Backward with Pruning (PFBP) algorithm for feature selection (FS) in Big Data settings (high dimensionality and/or sample size). To tackle the challenges of Big Data FS PFBP partitions the data matrix both in terms of rows (samples, training examples) as well as […]
[arXiv] BigDataFr recommends: Strategies for Big Data Analytics through Lambda Architectures in Volatile Environments
BigDataFr recommends: Strategies for Big Data Analytics through Lambda Architectures in Volatile Environments […] Expectations regarding the future growth of Internet of Things (IoT)-related technologies are high. These expectations require the realization of a sustainable general purpose application framework that is capable to handle these kinds of environments with their complexity in terms of heterogeneity […]
[Datasciencecentral] BigDataFr recommends: More on Fully Automated Machine Learning
BigDataFr recommends: More on Fully Automated Machine Learning […] Recently we’ve written a series of articles on Automated Machine Learning (AML) which are platforms or packages designed to take over the most repetitive elements of preparing predictive models. Typically these cover cleaning, preprocessing, some feature engineering, feature selection, and then model creation using one or […]
[arXiv – Ariane Carrance] BigDataFr recommends: Uniform random colored complexes
BigDataFr recommends: Uniform random colored complexes […] We present here random distributions on (D+1)-edge-colored, bipartite graphs with a fixed number of vertices 2p. These graphs are dual to D-dimensional orientable colored complexes. We investigate the behavior of quantities related to those random graphs, such as their number of connected components or the number of vertices […]