Journal ArticleDOI
Recognizing composite daily activities from crowd-labelled social media data
TLDR
This work takes an alternative approach of leveraging rich, dynamic, and crowd-generated self-report data from social media platforms as the basis for in-situ activity recognition using instrumentation-free, crowdsourced, social media data.About:
This article is published in Pervasive and Mobile Computing.The article was published on 2016-02-01. It has received 17 citations till now. The article focuses on the topics: Activity recognition & Population.read more
Citations
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Journal ArticleDOI
Social Media Analytics: Literature Review and Directions for Future Research
TL;DR: A comprehensive review of the SMA empirical literature and directions for future research suggests that novel methods, such as cross-media data classification, tags detection, label priority ranking, tweeting activity signatures, and geospatial data processing have been used less and could be further explored in future research.
Journal ArticleDOI
Automatic Processing of User-Generated Content for the Description of Energy-Consuming Activities at Individual and Group Level
TL;DR: This paper designs an ontology to get a better understanding of the energy-consuming activities domain and develops a text and image processing pipeline to extract from social media the description of energy- consuming activities.
Proceedings ArticleDOI
Understanding Context for Tasks and Activities
TL;DR: This work proposes a method for extraction of an extensive set of open-vocabulary activities from Twitter and establishes spatiotemporal models of these activities at the time of posting, and solves predictive tasks centered on the relationship between activity and spatiotmporal context that are aimed at supporting an individual's information needs.
Journal ArticleDOI
Developing an optimised activity type annotation method based on classification accuracy and entropy indices
Wim Ectors,Sofie Reumers,Won Do Lee,Keechoo Choi,Bruno Kochan,Davy Janssens,Tom Bellemans,Geert Wets +7 more
TL;DR: This study developed an optimised classification methodology by identifying classes of activity types with an optimal balance between improving model accuracy, and preserving activity information from the original data set.
Journal ArticleDOI
Consume: A privacy-preserving authorisation and authentication service for connecting with health and wellbeing APIs
TL;DR: Consume is a service for aggregating authentication and authorisation for Application Programming Interfaces (APIs) and allows to add existing and custom APIs on-the-fly without restarting services.
References
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Journal Article
Scikit-learn: Machine Learning in Python
Fabian Pedregosa,Gaël Varoquaux,Alexandre Gramfort,Vincent Michel,Bertrand Thirion,Olivier Grisel,Mathieu Blondel,Peter Prettenhofer,Ron Weiss,Vincent Dubourg,Jake Vanderplas,Alexandre Passos,David Cournapeau,Matthieu Brucher,Matthieu Perrot,Edouard Duchesnay +15 more
TL;DR: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems, focusing on bringing machine learning to non-specialists using a general-purpose high-level language.
Posted Content
Scikit-learn: Machine Learning in Python
Fabian Pedregosa,Gaël Varoquaux,Alexandre Gramfort,Vincent Michel,Bertrand Thirion,Olivier Grisel,Mathieu Blondel,Andreas Müller,Joel Nothman,Gilles Louppe,Peter Prettenhofer,Ron Weiss,Vincent Dubourg,Jake Vanderplas,Alexandre Passos,David Cournapeau,Matthieu Brucher,Matthieu Perrot,Edouard Duchesnay +18 more
TL;DR: Scikit-learn as mentioned in this paper is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems.
Book ChapterDOI
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
TL;DR: This paper explores the use of Support Vector Machines for learning text classifiers from examples and analyzes the particular properties of learning with text data and identifies why SVMs are appropriate for this task.
Journal ArticleDOI
Gene Selection for Cancer Classification using Support Vector Machines
TL;DR: In this article, a Support Vector Machine (SVM) method based on recursive feature elimination (RFE) was proposed to select a small subset of genes from broad patterns of gene expression data, recorded on DNA micro-arrays.
Journal Article
LIBLINEAR: A Library for Large Linear Classification
TL;DR: LIBLINEAR is an open source library for large-scale linear classification that supports logistic regression and linear support vector machines and provides easy-to-use command-line tools and library calls for users and developers.