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Статті в журналах з теми "Artificial symbol learning"
Pollack, Courtney. "Same-different judgments with alphabetic characters: The case of literal symbol processing." Journal of Numerical Cognition 5, no. 2 (August 22, 2019): 241–59. http://dx.doi.org/10.5964/jnc.v5i2.163.
Повний текст джерелаAhmetoglu, Alper, M. Yunus Seker, Justus Piater, Erhan Oztop, and Emre Ugur. "DeepSym: Deep Symbol Generation and Rule Learning for Planning from Unsupervised Robot Interaction." Journal of Artificial Intelligence Research 75 (November 6, 2022): 709–45. http://dx.doi.org/10.1613/jair.1.13754.
Повний текст джерелаBaek, Sung-Bum, Jin-Gon Shon, and Ji-Su Park. "CAC: A Learning Context Recognition Model Based on AI for Handwritten Mathematical Symbols in e-Learning Systems." Mathematics 10, no. 8 (April 12, 2022): 1277. http://dx.doi.org/10.3390/math10081277.
Повний текст джерелаYANG, DERSHUNG, LARRY A. RENDELL, JULIE L. WEBSTER, DORIS S. SHAW, and JAMES H. GARRETT. "SYMBOL RECOGNITION IN A CAD ENVIRONMENT USING A NEURAL NETWORK." International Journal on Artificial Intelligence Tools 03, no. 02 (June 1994): 157–85. http://dx.doi.org/10.1142/s0218213094000091.
Повний текст джерелаCrespo, Kimberly, and Margarita Kaushanskaya. "The Role of Attention, Language Ability, and Language Experience in Children's Artificial Grammar Learning." Journal of Speech, Language, and Hearing Research 65, no. 4 (April 4, 2022): 1574–91. http://dx.doi.org/10.1044/2021_jslhr-21-00112.
Повний текст джерелаKITANI, KRIS M., YOICHI SATO, and AKIHIRO SUGIMOTO. "RECOVERING THE BASIC STRUCTURE OF HUMAN ACTIVITIES FROM NOISY VIDEO-BASED SYMBOL STRINGS." International Journal of Pattern Recognition and Artificial Intelligence 22, no. 08 (December 2008): 1621–46. http://dx.doi.org/10.1142/s0218001408006776.
Повний текст джерелаInkelas, Sharon, Keith Johnson, Charles Lee, Emil Minas, George Mulcaire, Gek Yong Keng, and Tomomi Yuasa. "Testing the Learnability of Writing Systems." Annual Meeting of the Berkeley Linguistics Society 39, no. 1 (December 16, 2013): 75. http://dx.doi.org/10.3765/bls.v39i1.3871.
Повний текст джерелаLatapie, Hugo, Ozkan Kilic, Gaowen Liu, Ramana Kompella, Adam Lawrence, Yuhong Sun, Jayanth Srinivasa, Yan Yan, Pei Wang, and Kristinn R. Thórisson. "A Metamodel and Framework for Artificial General Intelligence From Theory to Practice." Journal of Artificial Intelligence and Consciousness 08, no. 02 (April 22, 2021): 205–27. http://dx.doi.org/10.1142/s2705078521500119.
Повний текст джерелаRaue, Federico, Andreas Dengel, Thomas M. Breuel, and Marcus Liwicki. "Symbol Grounding Association in Multimodal Sequences with Missing Elements." Journal of Artificial Intelligence Research 61 (April 11, 2018): 787–806. http://dx.doi.org/10.1613/jair.5736.
Повний текст джерелаKocabas, S. "A review of learning." Knowledge Engineering Review 6, no. 3 (September 1991): 195–222. http://dx.doi.org/10.1017/s0269888900005804.
Повний текст джерелаДисертації з теми "Artificial symbol learning"
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 джерелаКниги з теми "Artificial symbol learning"
Apolloni, Bruno. From Synapses to Rules: Discovering Symbolic Rules from Neural Processed Data. Boston, MA: Springer US, 2002.
Знайти повний текст джерелаConference on Data Analysis, Learning Symbolic and Numeric Knowledge (1989 Antibes, France). Data analysis, learning symbolic and numeric knowledge: Proceedings of the Conference on Data Analysis, Learning Symbolic and Numeric Knowledge, Antibes, September 11-14, 1989. Commack, N.Y: Nova Science Publishers, 1989.
Знайти повний текст джерелаE, Diday, and Institut national de recherche en informatique et en automatique (France), eds. Data analysis, learning symbolic and numeric knowledge: Proceedings of the Conference on Data Analysis, Learning Symbolic and Numeric Knowledge, Antibes, September 11-14, 1989. Commack, N.Y: Nova Science Publishers, 1989.
Знайти повний текст джерелаGabbay, Dov M. Abductive Reasoning and Learning. Dordrecht: Springer Netherlands, 2000.
Знайти повний текст джерелаPascal, Hitzler, and SpringerLink (Online service), eds. Perspectives of Neural-Symbolic Integration. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2007.
Знайти повний текст джерелаInternational School on Neural Nets "E.R. Caianiello" Fifth Course: From Synapses to Rules: Discovering Symbolic Rules From Neural Processed Data (2002 Erice, Italy). From synapses to rules: Discovering symbolic rules from neural processed data. New York: Kluwer Academic/Plenum Pub., 2002.
Знайти повний текст джерелаLaurent, Miclet, De la Higuera Colin, and International Colloquium on Grammatical Inference (3rd : 1996 : Montpellier, France), eds. Grammatical inference: Learning syntax from sentences : Third International Colloquium, ICGI-96, Montpellier, France, September 25-27, 1996 : proceedings. Berlin: Springer, 1996.
Знайти повний текст джерелаPieter, Adriaans, Fernau Henning 1965-, and Zaanen Menno van 1972-, eds. Grammatical inference: Algorithms and applications : 6th international colloquium, ICGI 2002, Amsterdam, The Netherlands, September 23-25, 2002 : proceedings. New York: Springer, 2002.
Знайти повний текст джерелаProudfoot, Diane, and B. Jack Copeland. Artificial Intelligence. Edited by Eric Margolis, Richard Samuels, and Stephen P. Stich. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780195309799.013.0007.
Повний текст джерелаNeural-Symbolic Learning Systems. Springer, 2002.
Знайти повний текст джерелаЧастини книг з теми "Artificial symbol learning"
Jiang, Yaqing, Petros Papapanagiotou, and Jacques Fleuriot. "Machine Learning for Inductive Theorem Proving." In Artificial Intelligence and Symbolic Computation, 87–103. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99957-9_6.
Повний текст джерелаEppler, Wolfgang. "Symbolic Learning in Connectionist Production Systems." In Artificial Neural Nets and Genetic Algorithms, 257–64. Vienna: Springer Vienna, 1993. http://dx.doi.org/10.1007/978-3-7091-7533-0_38.
Повний текст джерелаRoque, Waldir L. "Learning qualitative physics reasoning from regime analysis." In Artificial Intelligence and Symbolic Mathematical Computing, 277–93. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/3-540-57322-4_20.
Повний текст джерелаGrumbach, Alain. "Learning at subsymbolic and symbolic levels." In Neural Networks: Artificial Intelligence and Industrial Applications, 91–94. London: Springer London, 1995. http://dx.doi.org/10.1007/978-1-4471-3087-1_18.
Повний текст джерелаStrannegård, Claes, Abdul Rahim Nizamani, and Ulf Persson. "A General System for Learning and Reasoning in Symbolic Domains." In Artificial General Intelligence, 174–85. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-09274-4_17.
Повний текст джерелаEsposito, F., D. Malerba, and G. Semeraro. "Incorporating statistical techniques into empirical symbolic learning systems." In Artificial Intelligence Frontiers in Statistics, 168–81. Boston, MA: Springer US, 1993. http://dx.doi.org/10.1007/978-1-4899-4537-2_14.
Повний текст джерелаKolonin, Anton. "Neuro-Symbolic Architecture for Experiential Learning in Discrete and Functional Environments." In Artificial General Intelligence, 106–15. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-93758-4_12.
Повний текст джерелаAyeb, Kawther Khazri, Yosra Meguebli, and Afef Kacem Echi. "Deep Learning Architecture for Off-Line Recognition of Handwritten Math Symbols." In Pattern Recognition and Artificial Intelligence, 200–214. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71804-6_15.
Повний текст джерелаRaue, Federico, Sebastian Palacio, Thomas M. Breuel, Wonmin Byeon, Andreas Dengel, and Marcus Liwicki. "Symbolic Association Using Parallel Multilayer Perceptron." In Artificial Neural Networks and Machine Learning – ICANN 2016, 347–54. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-44781-0_41.
Повний текст джерелаDillmann, R., and H. Friedrich. "Programming by demonstration: A machine learning approach to support skill acquision for robots." In Artificial Intelligence and Symbolic Mathematical Computation, 87–108. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/3-540-61732-9_52.
Повний текст джерелаТези доповідей конференцій з теми "Artificial symbol learning"
Liang, Xiaoyuan, Martin Renqiang Min, Hongyu Guo, and Guiling Wang. "Learning K-way D-dimensional Discrete Embedding for Hierarchical Data Visualization and Retrieval." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/411.
Повний текст джерелаAlomari, Muhannad, Paul Duckworth, Nils Bore, Majd Hawasly, David C. Hogg, and Anthony G. Cohn. "Grounding of Human Environments and Activities for Autonomous Robots." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/193.
Повний текст джерелаGurina, Ekaterina, Ksenia Antipova, Nikita Klyuchnikov, and Dmitry Koroteev. "Machine Learning Microservice for Identification of Accident Predecessors." In SPE Middle East Oil & Gas Show and Conference. SPE, 2021. http://dx.doi.org/10.2118/204707-ms.
Повний текст джерелаDang, Long Hoang, Thao Minh Le, Vuong Le, and Truyen Tran. "Hierarchical Object-oriented Spatio-Temporal Reasoning for Video Question Answering." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/88.
Повний текст джерелаDumancic, Sebastijan, Alberto Garcia-Duran, and Mathias Niepert. "A Comparative Study of Distributional and Symbolic Paradigms for Relational Learning." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/843.
Повний текст джерелаJames, Steven. "Learning Portable Symbolic Representations." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/826.
Повний текст джерелаRaczaszek-Leonardi, Joanna, and Terrence W. Deacon. "Ungrounding symbols in language development: implications for modeling emergent symbolic communication in artificial systems." In 2018 Joint IEEE 8th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob). IEEE, 2018. http://dx.doi.org/10.1109/devlrn.2018.8761016.
Повний текст джерелаYang, Fangkai, Daoming Lyu, Bo Liu, and Steven Gustafson. "PEORL: Integrating Symbolic Planning and Hierarchical Reinforcement Learning for Robust Decision-Making." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/675.
Повний текст джерелаDai, Wang-Zhou, and Stephen Muggleton. "Abductive Knowledge Induction from Raw Data." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/254.
Повний текст джерелаRaedt, Luc de, Sebastijan Dumančić, Robin Manhaeve, and Giuseppe Marra. "From Statistical Relational to Neuro-Symbolic Artificial Intelligence." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/688.
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