98,699 research outputs found
Meta-learning computational intelligence architectures
In computational intelligence, the term \u27memetic algorithm\u27 has come to be associated with the algorithmic pairing of a global search method with a local search method. In a sociological context, a \u27meme\u27 has been loosely defined as a unit of cultural information, the social analog of genes for individuals. Both of these definitions are inadequate, as \u27memetic algorithm\u27 is too specific, and ultimately a misnomer, as much as a \u27meme\u27 is defined too generally to be of scientific use. In this dissertation the notion of memes and meta-learning is extended from a computational viewpoint and the purpose, definitions, design guidelines and architecture for effective meta-learning are explored. The background and structure of meta-learning architectures is discussed, incorporating viewpoints from psychology, sociology, computational intelligence, and engineering. The benefits and limitations of meme-based learning are demonstrated through two experimental case studies -- Meta-Learning Genetic Programming and Meta- Learning Traveling Salesman Problem Optimization. Additionally, the development and properties of several new algorithms are detailed, inspired by the previous case-studies. With applications ranging from cognitive science to machine learning, meta-learning has the potential to provide much-needed stimulation to the field of computational intelligence by providing a framework for higher order learning --Abstract, page iii
Bayesian Active Meta-Learning for Reliable and Efficient AI-Based Demodulation
Two of the main principles underlying the life cycle of an artificial
intelligence (AI) module in communication networks are adaptation and
monitoring. Adaptation refers to the need to adjust the operation of an AI
module depending on the current conditions; while monitoring requires measures
of the reliability of an AI module's decisions. Classical frequentist learning
methods for the design of AI modules fall short on both counts of adaptation
and monitoring, catering to one-off training and providing overconfident
decisions. This paper proposes a solution to address both challenges by
integrating meta-learning with Bayesian learning. As a specific use case, the
problems of demodulation and equalization over a fading channel based on the
availability of few pilots are studied. Meta-learning processes pilot
information from multiple frames in order to extract useful shared properties
of effective demodulators across frames. The resulting trained demodulators are
demonstrated, via experiments, to offer better calibrated soft decisions, at
the computational cost of running an ensemble of networks at run time. The
capacity to quantify uncertainty in the model parameter space is further
leveraged by extending Bayesian meta-learning to an active setting. In it, the
designer can select in a sequential fashion channel conditions under which to
generate data for meta-learning from a channel simulator. Bayesian active
meta-learning is seen in experiments to significantly reduce the number of
frames required to obtain efficient adaptation procedure for new frames.Comment: To appear in IEEE Transactions on Signal Processin
Flexible Fuzzy Rule Bases Evolution with Swarm Intelligence for Meta-Scheduling in Grid Computing
Fuzzy rule-based systems are expert systems whose performance is strongly related to the quality of their knowledge and the associated knowledge acquisition processes and thus, the design of effective learning techniques is considered a critical and major problem of these systems. Knowledge acquisition with a swarm intelligence approach is a recent learning strategy for the evolution of fuzzy rule bases founded on swarm intelligence showing improvement over classical knowledge acquisition strategies in fuzzy rule based systems such as Pittsburgh and Michigan approaches in terms of convergence behaviour and accuracy. In this work, a generalization of this method is proposed to allow the simultaneous consideration of diversely configured knowledge bases and this way to accelerate the learning process of the original algorithm. In order to test the suggested strategy, a problem of practical importance nowadays, the design of expert meta-schedulers systems for grid computing is considered. Simulations results show the fact that the suggested adaptation improves the functionality of knowledge acquisition with a swarm intelligence approach and it reduces computational effort; at the same time it keeps the quality of the canonical strategy
Application of Computational Intelligence Techniques to Process Industry Problems
In the last two decades there has been a large progress in the computational
intelligence research field. The fruits of the effort spent on the research in the discussed
field are powerful techniques for pattern recognition, data mining, data modelling, etc.
These techniques achieve high performance on traditional data sets like the UCI
machine learning database. Unfortunately, this kind of data sources usually represent
clean data without any problems like data outliers, missing values, feature co-linearity,
etc. common to real-life industrial data. The presence of faulty data samples can have
very harmful effects on the models, for example if presented during the training of the
models, it can either cause sub-optimal performance of the trained model or in the worst
case destroy the so far learnt knowledge of the model. For these reasons the application
of present modelling techniques to industrial problems has developed into a research
field on its own. Based on the discussion of the properties and issues of the data and the
state-of-the-art modelling techniques in the process industry, in this paper a novel
unified approach to the development of predictive models in the process industry is
presented
A System for Accessible Artificial Intelligence
While artificial intelligence (AI) has become widespread, many commercial AI
systems are not yet accessible to individual researchers nor the general public
due to the deep knowledge of the systems required to use them. We believe that
AI has matured to the point where it should be an accessible technology for
everyone. We present an ongoing project whose ultimate goal is to deliver an
open source, user-friendly AI system that is specialized for machine learning
analysis of complex data in the biomedical and health care domains. We discuss
how genetic programming can aid in this endeavor, and highlight specific
examples where genetic programming has automated machine learning analyses in
previous projects.Comment: 14 pages, 5 figures, submitted to Genetic Programming Theory and
Practice 2017 worksho
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