1,528 research outputs found
Artificial Neurons with Arbitrarily Complex Internal Structures
Artificial neurons with arbitrarily complex internal structure are
introduced. The neurons can be described in terms of a set of internal
variables, a set activation functions which describe the time evolution of
these variables and a set of characteristic functions which control how the
neurons interact with one another. The information capacity of attractor
networks composed of these generalized neurons is shown to reach the maximum
allowed bound. A simple example taken from the domain of pattern recognition
demonstrates the increased computational power of these neurons. Furthermore, a
specific class of generalized neurons gives rise to a simple transformation
relating attractor networks of generalized neurons to standard three layer
feed-forward networks. Given this correspondence, we conjecture that the
maximum information capacity of a three layer feed-forward network is 2 bits
per weight.Comment: 22 pages, 2 figure
The Semantic Web Paradigm for a Real-Time Agent Control (Part I)
For the Semantic Web point of view, computers must have access to structured collections of information and sets of inference rules that they can use to conduct automated reasoning. Adding logic to the Web, the means to use rules to make inferences, choose courses of action and answer questions, is the actual task for the distributed IT community. The real power of Intelligent Web will be realized when people create many programs that collect Web content from diverse sources, process the information and exchange the results with other programs. The first part of this paper is an introductory of Semantic Web properties, and summarises agent characteristics and their actual importance in digital economy. The second part presents the predictability of a multiagent system used in a learning process for a control problem.Semantic Web, agents, fuzzy knowledge, evolutionary computing
A Word Embedding-Based Method for Unsupervised Adaptation of Cooking Recipes
Studying food recipes is indispensable to understand the science of cooking. An essential
problem in food computing is the adaptation of recipes to user needs and preferences. The main difficulty
when adapting recipes is in determining ingredients relations, which are compound and hard to interpret.
Word embedding models can catch the semantics of food items in a recipe, helping to understand how
ingredients are combined and substituted. In this work, we propose an unsupervised method for adapting
ingredient recipes to user preferences. To learn food representations and relations, we create and apply a
specific-domain word embedding model. In contrast to previous works, we not only use the list of ingredients
to train the model but also the cooking instructions. We enrich the ingredient data by mapping them to
a nutrition database to guide the adaptation and find ingredient substitutes. We performed three different
kinds of recipe adaptation based on nutrition preferences, adapting to similar ingredients, and vegetarian and
vegan diet restrictions. With a 95% of confidence, our method can obtain quality adapted recipes without a
previous knowledge extraction on the recipe adaptation domain. Our results confirm the potential of using a
specific-domain semantic model to tackle the recipe adaptation task.European Commission
816303University of Granad
Interactive and life-long learning for identification and categorization tasks
Abstract (engl.)
This thesis focuses on life-long and interactive learning for recognition tasks. To achieve these targets the separation into a short-term memory (STM) and a long-term memory (LTM) is proposed. For the incremental build up of the STM a similarity-based one-shot learning method was developed. Furthermore two consolidation algorithms were proposed enabling the incremental learning of LTM representations. Based on the Learning Vector Quantization (LVQ) network architecture an error-based node insertion rule and a node dependent learning rate are proposed to enable life-long learning. For learning of categories additionally a forward-feature selection method was introduced to separate co-occurring categories. In experiments the performance of these learning methods could be shown for difficult visual recognition problems
A Survey of Adaptive Resonance Theory Neural Network Models for Engineering Applications
This survey samples from the ever-growing family of adaptive resonance theory
(ART) neural network models used to perform the three primary machine learning
modalities, namely, unsupervised, supervised and reinforcement learning. It
comprises a representative list from classic to modern ART models, thereby
painting a general picture of the architectures developed by researchers over
the past 30 years. The learning dynamics of these ART models are briefly
described, and their distinctive characteristics such as code representation,
long-term memory and corresponding geometric interpretation are discussed.
Useful engineering properties of ART (speed, configurability, explainability,
parallelization and hardware implementation) are examined along with current
challenges. Finally, a compilation of online software libraries is provided. It
is expected that this overview will be helpful to new and seasoned ART
researchers
Intelligent flight control systems
The capabilities of flight control systems can be enhanced by designing them to emulate functions of natural intelligence. Intelligent control functions fall in three categories. Declarative actions involve decision-making, providing models for system monitoring, goal planning, and system/scenario identification. Procedural actions concern skilled behavior and have parallels in guidance, navigation, and adaptation. Reflexive actions are spontaneous, inner-loop responses for control and estimation. Intelligent flight control systems learn knowledge of the aircraft and its mission and adapt to changes in the flight environment. Cognitive models form an efficient basis for integrating 'outer-loop/inner-loop' control functions and for developing robust parallel-processing algorithms
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