Дисертації з теми "Artificial symbol learning"
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dePalma, Nicholas Brian. "Task transparency in learning by demonstration : gaze, pointing, and dialog." Thesis, Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/34702.
Повний текст джерелаDrummond, Chris. "A symbol's role in learning low-level control functions." Thesis, University of Ottawa (Canada), 1999. http://hdl.handle.net/10393/8886.
Повний текст джерелаChen, Hsinchun. "Machine Learning for Information Retrieval: Neural Networks, Symbolic Learning, and Genetic Algorithms." Wiley Periodicals, Inc, 1995. http://hdl.handle.net/10150/106427.
Повний текст джерелаInformation retrieval using probabilistic techniques has attracted significant attention on the part of researchers in information and computer science over the past few decades. In the 1980s, knowledge-based techniques also made an impressive contribution to “intelligent” information retrieval and indexing. More recently, information science researchers have turned to other newer artificial-intelligence- based inductive learning techniques including neural networks, symbolic learning, and genetic algorithms. These newer techniques, which are grounded on diverse paradigms, have provided great opportunities for researchers to enhance the information processing and retrieval capabilities of current information storage and retrieval systems. In this article, we first provide an overview of these newer techniques and their use in information science research. To familiarize readers with these techniques, we present three popular methods: the connectionist Hopfield network; the symbolic ID3/ID5R; and evolution- based genetic algorithms. We discuss their knowledge representations and algorithms in the context of information retrieval. Sample implementation and testing results from our own research are also provided for each technique. We believe these techniques are promising in their ability to analyze user queries, identify users’ information needs, and suggest alternatives for search. With proper user-system interactions, these methods can greatly complement the prevailing full-text, keywordbased, probabilistic, and knowledge-based techniques.
Chen, Hsinchun, P. Buntin, Linlin She, S. Sutjahjo, C. Sommer, and D. Neely. "Expert Prediction, Symbolic Learning, and Neural Networks: An Experiment on Greyhound Racing." IEEE, 1994. http://hdl.handle.net/10150/105472.
Повний текст джерелаFor our research, we investigated a different problem-solving scenario called game playing, which is unstructured, complex, and seldom-studied. We considered several real-life game-playing scenarios and decided on greyhound racing. The large amount of historical information involved in the search poses a challenge for both human experts and machine-learning algorithms. The questions then become: Can machine-learning techniques reduce the uncertainty in a complex game-playing scenario? Can these methods outperform human experts in prediction? Our research sought to answer these questions.
Fusting, Christopher Winter. "Temporal Feature Selection with Symbolic Regression." ScholarWorks @ UVM, 2017. http://scholarworks.uvm.edu/graddis/806.
Повний текст джерелаAlquézar, Mancho René. "Symbolic and connectionist learning techniques for grammatical inference." Doctoral thesis, Universitat Politècnica de Catalunya, 1997. http://hdl.handle.net/10803/6651.
Повний текст джерелаThe first part, introduction and review (Chapters 1 to 4), presents an extensive state-of-the-art review of both symbolic and connectionist GI methods, that serves also to state most of the basic material needed to describe later the contributions of the thesis. These contributions constitute the contents of the rest of parts (Chapters 5 to 10).
The second part, contributions on symbolic and connectionist techniques for regular grammatical inference (Chapters 5 to 7), describes the contributions related to the theory and methods for regular GI, which include other lateral subjects such as the representation oí. finite-state machines (FSMs) in recurrent neural networks (RNNs).
The third part of the thesis, augmented regular expressions and their inductive inference, comprises Chapters 8 and 9. The augmented regular expressions (or AREs) are defined and proposed as a new representation for a subclass of CSLs that does not contain all the context-free languages but a large class of languages capable of describing patterns with symmetries and other (context-sensitive) structures of interest in pattern recognition problems.
The fourth part of the thesis just includes Chapter 10: conclusions and future research. Chapter 10 summarizes the main results obtained and points out the lines of further research that should be followed both to deepen in some of the theoretical aspects raised and to facilitate the application of the developed GI tools to real-world problems in the area of computer vision.
Galassi, Andrea. "Symbolic versus sub-symbolic approaches: a case study on training Deep Networks to play Nine Men’s Morris game." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/12859/.
Повний текст джерелаGiuliani, Luca. "Extending the Moving Targets Method for Injecting Constraints in Machine Learning." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/23885/.
Повний текст джерелаMilaré, Claudia Regina. ""Extração de conhecimento de redes neurais artificiais utilizando sistemas de aprendizado simbólico e algoritmos genéticos"." Universidade de São Paulo, 2003. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-11082004-004358/.
Повний текст джерелаIn Machine Learning - ML there is not a single algorithm that is the best for all application domains. In practice, several research works have shown that Artificial Neural Networks - ANNs have an appropriate inductive bias for several domains. Thus, ANNs have been applied to a number of data sets with high predictive accuracy. Symbolic ML algorithms have a less flexible inductive bias than ANNs. While ANNs can learn any input-output mapping, i.e., ANNs have the universal approximation property, symbolic ML algorithms frequently learn concepts describing them using hyperplanes. On the other hand, symbolic algorithms are needed when a good understating of the decision process is essential, since symbolic ML algorithms express the knowledge induced using symbolic structures that can be interpreted and understood by humans. ANNs lack the capability of explaining their decisions since the knowledge is encoded as real-valued weights and biases of the network. This encoding is difficult to be interpreted by humans. In several application domains, such as credit approval and medical diagnosis, providing an explanation related to the classification given to a certain case is of crucial importance. In a similar way, several users of ML algorithms desire to validate the knowledge induced, in order to assure that the generalization made by the algorithm is correct. In order to apply ANNs to a larger number of application domains, several researches have proposed methods to extract comprehensible knowledge from ANNs. The primary contribution of this thesis consists of two methods that extract symbolic knowledge, expressed as decision rules, from ANNs. The proposed methods have several advantages over previous methods, such as being applicable to any architecture and supervised learning algorithm of ANNs. The first method uses standard symbolic ML algorithm to extract knowledge from ANNs, and the second method extends the first method by combining the knowledge induced by several symbolic ML algorithms through the application of a Genetic Algorithm - GA. The proposed methods are experimentally analyzed in a number of application domains. Results show that both methods are capable to extract symbolic knowledge having high fidelity with trained ANNs. The proposed methods are compared with TREPAN, showing promising results. TREPAN is a well known method to extract knowledge from ANNs.
Misino, Eleonora. "Deep Generative Models with Probabilistic Logic Priors." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/24058/.
Повний текст джерелаOsório, Fernando Santos. "Inss : un système hybride neuro-symbolique pour l'apprentissage automatique constructif." Grenoble INPG, 1998. https://tel.archives-ouvertes.fr/tel-00004899.
Повний текст джерелаVarious Artificial Intelligence methods have been developed to reproduce intelligent human behaviour. These methods allow to reproduce some human reasoning process using the available knowledge. Each method has its advantages, but also some drawbacks. Hybrid systems combine different approaches in order to take advantage of their respective strengths. These hybrid intelligent systems also present the ability to acquire new knowledge from different sources and so to improve their application performance. This thesis presents our research in the field of hybrid neuro-symbolic systems, and in particular the study of machine learning tools used for constructive knowledge acquisition. We are interested in the automatic acquisition of theoretical knowledge (rules) and empirical knowledge (examples). We present a new hybrid system we implemented: INSS - Incremental Neuro-Symbolic System. This system allows knowledge transfer from the symbolic module to the connectionist module (Artificial Neural Network - ANN), through symbolic rule compilation into an ANN. We can refine the initial ANN knowledge through neural learning using a set of examples. The incremental ANN learning method used, the Cascade-Correlation algorithm, allows us to change or to add new knowledge to the network. Then, the system can also extract modified (or new) symbolic rules from the ANN and validate them. INSS is a hybrid machine learning system that implements a constructive knowledge acquisition method. We conclude by showing the results we obtained with this system in different application domains: ANN artificial problems(The Monk's Problems), computer aided medical diagnosis (Toxic Comas), a cognitive modelling task (The Balance Scale Problem) and autonomous robot control. The results we obtained show the improved performance of INSS and its advantages over others hybrid neuro-symbolic systems
Smetka, Tomáš. "Kryptoanalýza symetrických šifrovacích algoritmů s využitím symbolické regrese a genetického programování." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2015. http://www.nusl.cz/ntk/nusl-234997.
Повний текст джерелаTürkmen, Ulas. "Situated Concepts and Pre-Linguistic Symbol Use." Doctoral thesis, 2010. https://repositorium.ub.uni-osnabrueck.de/handle/urn:nbn:de:gbv:700-201006076293.
Повний текст джерелаHuang, Guoxiang. "Automated reasoning and machine learning." Thesis, 1996. http://hdl.handle.net/10125/9963.
Повний текст джерелаHostičková, Iva. "Vývoj paradigmat výzkumu umělé inteligence." Master's thesis, 2014. http://www.nusl.cz/ntk/nusl-332251.
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