Information Theory Based Error and Regularization Functions in Artificial Intelligence Algorithms in the Big Data Era
A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Information Theory, Probability and Statistics".
Deadline for manuscript submissions: closed (15 December 2019) | Viewed by 8581
Special Issue Editors
Interests: machine learning; computational intelligence; data science; data mining; smart metering
Special Issues, Collections and Topics in MDPI journals
Interests: machine learning (statistical relational learning, tractable learning), knowledge representation and reasoning (graphical models, lifted probabilistic inference, knowledge compilation), applications of probabilistic reasoning and learning (probabilistic programming, probabilistic databases), and artificial intelligence
Special Issue Information
Dear Colleagues,
We all live in the era of emerging technological advancements. The days when almost everything was done manually are gone, and now we live in the time in which a lot of activities have been taken over by machines, software, and automatic processes. In this context, artificial intelligence (AI) has a special place in all the advancements that have been made. AI is applying science to computers and machines to develop intelligence like humans have. With this technology, machines are able to do some of the simple to complex tasks that humans do on a regular basis.
Today, the amount of data is exploding at an unprecedented rate as a result of developments in Web technologies, social media, and mobile and sensing devices. The concept of big data is defined by Gartner as high volume, high velocity, high variety, and high veracity data that require new processing paradigms to enable insight discovery, improved decision making, and process optimization. The potential of big data is highlighted by their definition; however, the realization of this potential depends on improving traditional approaches or developing new ones capable of handling such data. Because of their potential, big data have been referred to as a revolution that will transform how we live, work, and think. The main purpose of this revolution is to make use of large amounts of data to enable knowledge discovery and better decision making.
The ability to extract value from big data depends on data analytics, which can be done using AI systems. While AI provides significant support in various areas such as time series forecasting, fraud detection, and image recognition, the road to the excellence is long. This is because AI has not been able to overcome a number of challenges—especially in the big data era—that still stand in the way of progress.
The main scope of this Special Issue is to propose new methods that are applicable for various artificial intelligence algorithms, thus improving their quality, robustness, and handling of big data. This would be possible as a result of application of the novel and nonstandard error and regularization information theory-based functions in artificial intelligence algorithms.
Dr. Krzysztof Gajowniczek
Prof. Dr. Guy Van den Broeck
Guest Editors
Manuscript Submission Information
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Keywords
- Artificial intelligence
- Big data
- Computational intelligence
- Data mining
- Entopy
- Information theory
- Learning theory
