Knowledge-guided bayesian support vector machine for high-dimensional data with application to analysis of genomics data
Support vector machine (SVM) is a popular classification method for the analysis of wide
range of data including big data. Many SVM methods with feature selection have been …
range of data including big data. Many SVM methods with feature selection have been …
Bayesian non-linear support vector machine for high-dimensional data with incorporation of graph information on features
Support vector machine (SVM) is a popular classification method for analysis of high
dimensional data such as genomics data. Recently a number of linear SVM methods have …
dimensional data such as genomics data. Recently a number of linear SVM methods have …
A variability-aware design approach to the data analysis modeling process
MCV Tavares, P Alencar… - 2018 IEEE International …, 2018 - ieeexplore.ieee.org
The massive amount of current data has led to many different forms of data analysis
processes that aim to explore this data to uncover valuable insights. Methodologies to guide …
processes that aim to explore this data to uncover valuable insights. Methodologies to guide …
Improving data science projects by enriching analytical models with domain knowledge
H Zhang, U Roy, J Saltz - … Conference on Big Data (Big Data), 2018 - ieeexplore.ieee.org
Domain knowledge is very important to support the development of analytic models.
However, in today's data science projects, domain knowledge is typically documented, but …
However, in today's data science projects, domain knowledge is typically documented, but …
Biomedical data ensemble classification using random projections
SK Tasoulis, AG Vrahatis… - … Conference on Big …, 2018 - ieeexplore.ieee.org
Biomedicine is undergoing a revolution driven by the explosion of biomedical data, which
are generated by emerged medical imaging, sensor technologies and high-throughput …
are generated by emerged medical imaging, sensor technologies and high-throughput …
Domain-specific topic model for knowledge discovery through conversational agents in data intensive scientific communities
Machine learning techniques underlying Big Data analytics have the potential to benefit data
intensive communities in eg, bioinformatics and neuroscience domain sciences. Today's …
intensive communities in eg, bioinformatics and neuroscience domain sciences. Today's …
Augmenting software project managers with predictions from machine learning
B Schreck, S Mallapur, S Damle… - … Conference on Big …, 2018 - ieeexplore.ieee.org
Many businesses ("") across industries hire technology service providers (" providers") to
develop and maintain software applications. The provider in turn hires a team, distributed …
develop and maintain software applications. The provider in turn hires a team, distributed …
Collecting cyber threat intelligence from hacker forums via a two-stage, hybrid process using support vector machines and latent dirichlet allocation
Traditional security controls, such as firewalls, anti-virus and IDS, are ill-equipped to help IT
security and response teams keep pace with the rapid evolution of the cyber threat …
security and response teams keep pace with the rapid evolution of the cyber threat …
Automated extraction of personal knowledge from smartphone push notifications
Personalized services are in need of a rich and powerful personal knowledge base, ie a
knowledge base containing information about the user. This paper proposes an approach to …
knowledge base containing information about the user. This paper proposes an approach to …
eTRIKS analytical environment: A modular high performance framework for medical data analysis
A Oehmichen, F Guitton, K Sun, J Grizet… - … Conference on Big …, 2017 - ieeexplore.ieee.org
Translational research is quickly becoming a science driven by big data. Improving patient
care, developing personalized therapies and new drugs depend increasingly on an …
care, developing personalized therapies and new drugs depend increasingly on an …