BigDataFr recommends: Statistical Challenges of Big Brain Network Data […] Subjects: Neurons and Cognition (q-bio.NC); Methodology (stat.ME) We explore the main characteristics of big brain network data that offer unique statistical challenges. The brain networks are biologically expected to be both sparse and hierarchical. Such unique characterizations put specific topological constraints onto statistical approaches and […]
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[Datasciencecentral] BigDataFr recommends: The Role of AI in Assisting Customer Experience
BigDataFr recommends: The Role of AI in Assisting Customer Experience […] From being the plots of sci-fi thrillers to being seen as threats by the working populace, Artificial Intelligence (AI) has during the last few years jumped into the headlines as it has become a part of reality. People are confused – AI is either […]
[arXiv] BigDataFr recommends: Improving Viability of Electric Taxis by Taxi Service Strategy Optimization
BigDataFr recommends: Improving Viability of Electric Taxis by Taxi Service Strategy Optimization: A Big Data Analysis of New York City […] Subjects: Computers and Society (cs.CY) Electrification of transportation is critical for a low-carbon society. In particular, public vehicles (e.g., taxis) provide a crucial opportunity for electrification. Despite the benefits of eco-friendliness and energy efficiency, […]
[Datasciencecentral] BigDataFr recommends: Representation of Numbers with Incredibly Fast Converging Fractions
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 […]
[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 […]