Machine Learning - IEEE Conferences, Publications, and.
Finally, a matrix relating the challenges and approaches is presented. Through this process, this paper provides a perspective on the domain, identifies research gaps and opportunities, and provides a strong foundation and encouragement for further research in the field of machine learning with Big Data. View this article on IEEE Xplore.
Latest thesis topics in Machine Learning for research scholars: Choosing a research and thesis topics in Machine Learning is the first choice of masters and Doctorate scholars now a days. Though, choosing and working on a thesis topic in machine learning is not an easy task as Machine learning uses certain statistical algorithms to make computers work in a certain way without being explicitly.
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Democratize deep learning: Pushing the limit on deep learning’s accuracy remains an exciting area of research, but as the saying goes, “perfect is the enemy of good.” Existing models are.
From its institution as the Neural Networks Council in the early 1990s, the IEEE Computational Intelligence Society has rapidly grown into a robust community with a vision for addressing real-world issues with biologically-motivated computational paradigms. The Society offers leading research in nature-inspired problem solving, including neural networks, evolutionary algorithms, fuzzy systems.
In this paper, we study the usage of machine-learning models for sales predictive analytics. The main goal of this paper is to consider main approaches and case studies of using machine learning.
WS-19: Open Workshop on Machine Learning in Communications We are excited to host the 3rd workshop on Machine Learning for Communications (ML4COMM), and to introduce a key new focus on openness and reproducibility, which IEEE is committed to promoting further as is absolutely neccisary in the growth of communications as a rigorous and reproducible application area of machine learning.