[arXiv] BigDataFr recommends: Human-Algorithm Interaction Biases in the Big Data Cycle: A Markov Chain Iterated Learning Framework

BigDataFr recommends: Human-Algorithm Interaction Biases in the Big Data Cycle: A Markov Chain Iterated Learning Framework Comments: This research was supported by National Science Foundation grant NSF-1549981 Subjects: Learning (cs.LG); Human-Computer Interaction (cs.HC) […] Early supervised machine learning algorithms have relied on reliable expert labels to build predictive models. However, the gates of data generation […]

[Datasciencecentral] BigDataFr recommends: A methodology for solving problems with DataScience for Internet of Things – Full – #iot

BigDataFr recommends: A methodology for solving problems with DataScience for Internet of Things – Part 1 and 2 […] This two part blog is based on my forthcoming book: Data Science for Internet of Things. It is also the basis for the course I teach Data Science for Internet of Things Course. I will be […]

[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 […]

[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 […]