BigDataFr recommends: Representation of functions on big data associated with directed graphs Subjects: Classical Analysis and ODEs (math.CA) […] This paper is an extension of the previous work of Chui, Filbir, and Mhaskar (Appl. Comput. Harm. Anal. 38 (3) 2015:489-509), not only from numeric data to include non-numeric data as in that paper, but also […]
Author: Big Data
[Datasciencecentral] BigDataFr recommends: Ableism in the Numbers – Social Metrification #datascientist
BigDataFr recommends: Ableism in the Numbers – Social Metrification […] Ableism (able + ism) is apparent in many interactions between people. While driving on a road having a posted limit of 60 KPH, I was traveling slower since I expected a red light to soon appear ahead of me. The driver behind me – at […]
[arXiv] BigDataFr recommends: Measuring Economic Activities of China with Mobile Big Data
BigDataFr recommends: Measuring Economic Activities of China with Mobile Big Data Subjects: Social and Information Networks (cs.SI); Computers and Society (cs.CY) […] Emerging trends in smartphones, online maps, social media, and the resulting geo-located data, provide opportunities to collect traces of people’s socio-economical activities in a much more granular and direct fashion, triggering a revolution […]
[Datasciencecentral] BigDataFr recommends: Applications of Deep Learning
BigDataFr recommends: Applications of Deep Learning […] This post highlights a number of important applications found for deep learning so far. It is well known that 80% of data is unstructured. Unstructured data is the messy stuff every quantitative analyst tries to traditionally stay away from. It can include images of accidents, text notes of […]
[arXiv] BigDataFr recommends: Big IoT and social networking data for smart cities: Algorithmic improvements on Big Data Analysis
BigDataFr recommends: Big IoT and social networking data for smart cities: Algorithmic improvements on Big Data Analysis in the context of RADICAL city applications Subjects: Computers and Society (cs.CY); Learning (cs.LG); Social and Information Networks (cs.SI) […] In this paper we present a SOA (Service Oriented Architecture)-based platform, enabling the retrieval and analysis of big […]
[Datasciencecentral – Top] BigDataFr recommends: 40 Techniques Used by Data Scientists
BigDataFr recommends: 40 Techniques Used by Data Scientists […] These techniques cover most of what data scientists and related practitioners are using in their daily activities, whether they use solutions offered by a vendor, or whether they design proprietary tools. When you click on any of the 40 links below, you will find a selection […]
[arXiv] BigDataFr recommends: Limited Random Walk Algorithm for Big Graph Data Clustering
BigDataFr recommends: Limited Random Walk Algorithm for Big Graph Data Clustering Subjects: Social and Information Networks (cs.SI); Physics and Society (physics.soc-ph) […]Graph clustering is an important technique to understand the relationships between the vertices in a big graph. In this paper, we propose a novel random-walk-based graph clustering method. The proposed method restricts the reach […]
[Datasciencecentral] BigDataFr recommends: The challenges of word embeddings #deeplearning
BigDataFr recommends: The challenges of word embeddings […] In recent times deep learning techniques have become more and more prevalent in NLP tasks; just take a look at the list of accepted papers at this year’s NAACL conference, and you can’t miss it. We’ve now completely moved away from traditional NLP approaches to focus on […]
[Datasciencecentral] BigDataFr recommends: Hitchhiker’s Guide to Data Science, Machine Learning, R, Python
BigDataFr recommends: Hitchhiker’s Guide to Data Science, Machine Learning, R, Python […] Thousands of articles and tutorials have been written about data science and machine learning. Hundreds of books, courses and conferences are available. You could spend months just figuring out what to do to get started, even to understand what data science is about. […]
[arXiv – MSc Thesis] BigDataFr recommends: LLFR: A Lanczos-Based Latent Factor Recommender for Big Data Scenarios
BigDataFr recommends: LLFR: A Lanczos-Based Latent Factor Recommender for Big Data Scenarios Subjects: Machine Learning (stat.ML); Information Retrieval (cs.IR); Social and Information Networks (cs.SI) […]The purpose if this master’s thesis is to study and develop a new algorithmic framework for Collaborative Filtering to produce recommendations in the top-N recommendation problem. Thus, we propose Lanczos Latent […]