Academic literature on the topic 'The Knowledge Machine'
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Journal articles on the topic "The Knowledge Machine"
Zenkert, Johannes, Christian Weber, Mareike Dornhöfer, Hasan Abu-Rasheed, and Madjid Fathi. "Knowledge Integration in Smart Factories." Encyclopedia 1, no. 3 (August 16, 2021): 792–811. http://dx.doi.org/10.3390/encyclopedia1030061.
Full textNirenburg, Sergei. "Knowledge-based machine translation." Machine Translation 4, no. 1 (March 1989): 5–24. http://dx.doi.org/10.1007/bf00367750.
Full textWalch, Michael, and Dimitris Karagiannis. "Design Thinking and Knowledge Engineering: A Machine Learning Case." International Journal of Machine Learning and Computing 10, no. 6 (December 2020): 765–70. http://dx.doi.org/10.18178/ijmlc.2020.10.6.1003.
Full textRefenes, Apostolos N. "Parallelism in knowledge-based machines." Knowledge Engineering Review 4, no. 1 (March 1989): 53–71. http://dx.doi.org/10.1017/s0269888900004744.
Full textLi, Fashen, Lian Li, Jianping Yin, Liang Huang, Qingguo Zhou, Ning An, Yong Zhang, et al. "Machine knowledge and human cognition." Big Data Mining and Analytics 3, no. 4 (December 2020): 292–99. http://dx.doi.org/10.26599/bdma.2020.9020009.
Full textBergadano, F., Y. Kodratoff, and K. Morik. "Machine Learning and Knowledge Acquisition." AI Communications 5, no. 1 (1992): 19–24. http://dx.doi.org/10.3233/aic-1992-5102.
Full textTandon, Niket, Aparna S. Varde, and Gerard de Melo. "Commonsense Knowledge in Machine Intelligence." ACM SIGMOD Record 46, no. 4 (February 22, 2018): 49–52. http://dx.doi.org/10.1145/3186549.3186562.
Full textBenker, H., J. M. Beacco, M. Dorochevsky, Th Jeffré, A. Pöhlmann, J. Noyé, B. Poterie, J. C. Syre, O. Thibault, and G. Watzlawik. "KCM: a knowledge crunching machine." ACM SIGARCH Computer Architecture News 17, no. 3 (June 1989): 186–94. http://dx.doi.org/10.1145/74926.74947.
Full textSuer, Gursel A., and Cihan Dagli. "Knowledge-based single machine scheduling." Computers & Industrial Engineering 23, no. 1-4 (November 1992): 149–52. http://dx.doi.org/10.1016/0360-8352(92)90085-x.
Full textLi, Fashen, Lian Li, Jianping Yin, Yong Zhang, Qingguo Zhou, and Kun Kuang. "How to Interpret Machine Knowledge." Engineering 6, no. 3 (March 2020): 218–20. http://dx.doi.org/10.1016/j.eng.2019.11.013.
Full textDissertations / Theses on the topic "The Knowledge Machine"
Ingevall, Markus. "Extending the Knowledge Machine." Thesis, Linköping University, Department of Computer and Information Science, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-2427.
Full textThis master's thesis deals with a frame-based knowledge representa- tion language and system called The Knowledge Machine (KM), de- veloped by Peter Clark and Bruce Porter at the University of Texas at Austin. The purpose of the thesis is to show a number of ways of changing and extending KM to handle larger classes of reasoning tasks associated with reasoning about actions and change.
Gispert, Ramis Adrià. "Introducing linguistic knowledge into statistical machine translation." Doctoral thesis, Universitat Politècnica de Catalunya, 2007. http://hdl.handle.net/10803/6902.
Full textEl sistema de traducció estocàstica utilitzat en aquest treball segueix un enfocament basat en tuples, unitats bilingües que permeten estimar un model de traducció de probabilitat conjunta per mitjà de la combinació, dins un entorn log-linial, de cadenes d'n-grames i funcions característiques addicionals. Es presenta un estudi detallat d'aquesta aproximació, que inclou la seva transformació des d'una implementació d'X-grames en autòmats d'estats finits, més orientada a la traducció de veu, cap a l'actual solució d'n-grames orientada a la traducció de text de gran vocabulari. La tesi estudia també les fases d'entrenament i decodificació, així com el rendiment per a diferents tasques (variant el tamany dels corpora o el parell d'idiomes) i els principals problemes reflectits en les anàlisis d'error.
La tesis també investiga la incorporació de informació lingüística específicament en aliniament per paraules. Es proposa l'extensió mitjançant classificació de formes verbals d'un algorisme d'aliniament paraula a paraula basat en co-ocurrències, amb resultats positius. Així mateix, s'avalua de forma empírica l'impacte en qualitat d'aliniament i de traducció que s'obté mitjançant l'etiquetatge morfològic, la lematització, la classificació de formes verbals i el truncament o stemming del text paral·lel.
Pel que fa al model de traducció, es proposa un model de tractament de les formes verbals per mitjà d'un model de instanciació addicional, i es realitzen experiments en la direcció d'anglès a castellà. La tesi també introdueix un model de llenguatge d'etiquetes morfològiques del destí per tal d'abordar problemes de concordança. Finalment, s'estudia l'impacte de la derivació morfològica en la formulació de la traducció estocàstica mitjançant n-grames, avaluant empíricament el possible guany derivat d'estratègies de reducció morfològica.
This Ph.D. thesis dissertation addresses the use of morphosyntactic information in order to improve the performance of Statistical Machine Translation (SMT) systems, providing them with additional linguistic information beyond the surface level of words from parallel corpora.
The statistical machine translation system in this work here follows a tuple-based approach, modelling joint-probability translation models via log-linear combination of bilingual n-grams with additional feature functions. A detailed study of the approach is conducted. This includes its initial development from a speech-oriented Finite-State Transducer architecture implementing X-grams towards a large-vocabulary text-oriented n-grams implementation, training and decoding particularities, portability across language pairs and tasks, and main difficulties as revealed in error analyses.
The use of linguistic knowledge to improve word alignment quality is also studied. A cooccurrence-based one-to-one word alignment algorithm is extended with verb form classification with successful results. Additionally, we evaluate the impact in word alignment and translation quality of Part-Of-Speech, base form, verb form classification and stemming on state-of-art word alignment tools.
Furthermore, the thesis proposes a translation model tackling verb form generation through an additional verb instance model, reporting experiments in English-to-Spanish tasks. Disagreement is addressed via incorporating a target Part-Of-Speech language model. Finally, we study the impact of morphology derivation on Ngram-based SMT formulation, empirically evaluating the quality gain that is to be gained via morphology reduction.
Berry, David T. "A knowledge-based framework for machine vision." Thesis, Heriot-Watt University, 1987. http://hdl.handle.net/10399/1022.
Full textZbib, Rabih M. (Rabih Mohamed) 1974. "Using linguistic knowledge in statistical machine translation." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/62391.
Full textCataloged from PDF version of thesis.
Includes bibliographical references (p. 153-162).
In this thesis, we present methods for using linguistically motivated information to enhance the performance of statistical machine translation (SMT). One of the advantages of the statistical approach to machine translation is that it is largely language-agnostic. Machine learning models are used to automatically learn translation patterns from data. SMT can, however, be improved by using linguistic knowledge to address specific areas of the translation process, where translations would be hard to learn fully automatically. We present methods that use linguistic knowledge at various levels to improve statistical machine translation, focusing on Arabic-English translation as a case study. In the first part, morphological information is used to preprocess the Arabic text for Arabic-to-English and English-to-Arabic translation, which reduces the gap in the complexity of the morphology between Arabic and English. The second method addresses the issue of long-distance reordering in translation to account for the difference in the syntax of the two languages. In the third part, we show how additional local context information on the source side is incorporated, which helps reduce lexical ambiguity. Two methods are proposed for using binary decision trees to control the amount of context information introduced. These methods are successfully applied to the use of diacritized Arabic source in Arabic-to-English translation. The final method combines the outputs of an SMT system and a Rule-based MT (RBMT) system, taking advantage of the flexibility of the statistical approach and the rich linguistic knowledge embedded in the rule-based MT system.
by Rabih M. Zbib.
Ph.D.in Information Technology
Hall, Joseph Alexander. "Machine learning for control : incorporating prior knowledge." Thesis, University of Cambridge, 2013. https://www.repository.cam.ac.uk/handle/1810/283930.
Full textHasan, Irfan. "Machine learning techniques for automated knowledge acquisition in intelligent knowledge-based systems." Instructions for remote access. Click here to access this electronic resource. Access available to Kutztown University faculty, staff, and students only, 1991. http://www.kutztown.edu/library/services/remote_access.asp.
Full textSource: Masters Abstracts International, Volume: 45-06, page: 3187. Abstract precedes thesis as [2] preliminary leaves. Typescript. Includes bibliographical references (leaves 102-104).
Mallen, Jason. "Utilising incomplete domain knowledge in an information theoretic guided inductive knowledge discovery algorithm." Thesis, University of Portsmouth, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.295773.
Full textSnyders, Sean. "Inductive machine learning bias in knowledge-based neurocomputing." Thesis, Stellenbosch : Stellenbosch University, 2003. http://hdl.handle.net/10019.1/53463.
Full textENGLISH ABSTRACT: The integration of symbolic knowledge with artificial neural networks is becoming an increasingly popular paradigm for solving real-world problems. This paradigm named knowledge-based neurocomputing, provides means for using prior knowledge to determine the network architecture, to program a subset of weights to induce a learning bias which guides network training, and to extract refined knowledge from trained neural networks. The role of neural networks then becomes that of knowledge refinement. It thus provides a methodology for dealing with uncertainty in the initial domain theory. In this thesis, we address several advantages of this paradigm and propose a solution for the open question of determining the strength of this learning, or inductive, bias. We develop a heuristic for determining the strength of the inductive bias that takes the network architecture, the prior knowledge, the learning method, and the training data into consideration. We apply this heuristic to well-known synthetic problems as well as published difficult real-world problems in the domain of molecular biology and medical diagnoses. We found that, not only do the networks trained with this adaptive inductive bias show superior performance over networks trained with the standard method of determining the strength of the inductive bias, but that the extracted refined knowledge from these trained networks deliver more concise and accurate domain theories.
AFRIKAANSE OPSOMMING: Die integrasie van simboliese kennis met kunsmatige neurale netwerke word 'n toenemende gewilde paradigma om reelewereldse probleme op te los. Hierdie paradigma genoem, kennis-gebaseerde neurokomputasie, verskaf die vermoe om vooraf kennis te gebruik om die netwerkargitektuur te bepaal, om a subversameling van gewigte te programeer om 'n leersydigheid te induseer wat netwerkopleiding lei, en om verfynde kennis van geleerde netwerke te kan ontsluit. Die rol van neurale netwerke word dan die van kennisverfyning. Dit verskaf dus 'n metodologie vir die behandeling van onsekerheid in die aanvangsdomeinteorie. In hierdie tesis adresseer ons verskeie voordele wat bevat is in hierdie paradigma en stel ons 'n oplossing voor vir die oop vraag om die gewig van hierdie leer-, of induktiewe sydigheid te bepaal. Ons ontwikkel 'n heuristiek vir die bepaling van die induktiewe sydigheid wat die netwerkargitektuur, die aanvangskennis, die leermetode, en die data vir die leer proses in ag neem. Ons pas hierdie heuristiek toe op bekende sintetiese probleme so weI as op gepubliseerde moeilike reelewereldse probleme in die gebied van molekulere biologie en mediese diagnostiek. Ons bevind dat, nie alleenlik vertoon die netwerke wat geleer is met die adaptiewe induktiewe sydigheid superieure verrigting bo die netwerke wat geleer is met die standaardmetode om die gewig van die induktiewe sydigheid te bepaal nie, maar ook dat die verfynde kennis wat ontsluit is uit hierdie geleerde netwerke meer bondige en akkurate domeinteorie lewer.
Lazzarini, Nicola. "Knowledge extraction from biomedical data using machine learning." Thesis, University of Newcastle upon Tyne, 2017. http://hdl.handle.net/10443/3839.
Full textPickard, Nigel Brougham. "The development of fuzzy knowledge base for machine monitoring." Thesis, University of Bristol, 1989. http://hdl.handle.net/1983/f4b76b81-7da1-494c-9cfa-3f21d62f9a48.
Full textBooks on the topic "The Knowledge Machine"
Holzinger, Andreas, Peter Kieseberg, A. Min Tjoa, and Edgar Weippl, eds. Machine Learning and Knowledge Extraction. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-66808-6.
Full textHolzinger, Andreas, Peter Kieseberg, A. Min Tjoa, and Edgar Weippl, eds. Machine Learning and Knowledge Extraction. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-57321-8.
Full textHolzinger, Andreas, Peter Kieseberg, A. Min Tjoa, and Edgar Weippl, eds. Machine Learning and Knowledge Extraction. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-29726-8.
Full textHolzinger, Andreas, Peter Kieseberg, A. Min Tjoa, and Edgar Weippl, eds. Machine Learning and Knowledge Extraction. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99740-7.
Full textHolzinger, Andreas, Peter Kieseberg, A. Min Tjoa, and Edgar Weippl, eds. Machine Learning and Knowledge Extraction. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-84060-0.
Full textKovalerchuk, Boris. Visual Knowledge Discovery and Machine Learning. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-73040-0.
Full textBalcázar, José Luis, Francesco Bonchi, Aristides Gionis, and Michèle Sebag, eds. Machine Learning and Knowledge Discovery in Databases. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15880-3.
Full textBalcázar, José Luis, Francesco Bonchi, Aristides Gionis, and Michèle Sebag, eds. Machine Learning and Knowledge Discovery in Databases. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15883-4.
Full textBalcázar, José Luis, Francesco Bonchi, Aristides Gionis, and Michèle Sebag, eds. Machine Learning and Knowledge Discovery in Databases. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15939-8.
Full textHolzinger, Andreas, Randy Goebel, Massimo Ferri, and Vasile Palade, eds. Towards Integrative Machine Learning and Knowledge Extraction. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-69775-8.
Full textBook chapters on the topic "The Knowledge Machine"
Vermeulen, Andreas François. "Background Knowledge." In Industrial Machine Learning, 7–12. Berkeley, CA: Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-5316-8_2.
Full textMannor, Shie, Xin Jin, Jiawei Han, Xin Jin, Jiawei Han, Xin Jin, Jiawei Han, and Xinhua Zhang. "Knowledge Discovery." In Encyclopedia of Machine Learning, 570. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_435.
Full textCamastra, Francesco, and Alessandro Vinciarelli. "Machine Learning." In Advanced Information and Knowledge Processing, 99–106. London: Springer London, 2015. http://dx.doi.org/10.1007/978-1-4471-6735-8_4.
Full textShanahan, James G. "Machine Learning." In Soft Computing for Knowledge Discovery, 143–75. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-4335-0_7.
Full textJohnson, Michael L. "Memory, Learning, Self-Knowledge." In Mind, Language, Machine, 232–41. London: Palgrave Macmillan UK, 1988. http://dx.doi.org/10.1007/978-1-349-19404-9_35.
Full textGill, Satinder P. "Designing for Knowledge Transfer." In Human Machine Symbiosis, 313–60. London: Springer London, 1996. http://dx.doi.org/10.1007/978-1-4471-3247-9_8.
Full textHoffmann, Achim, and Ashesh Mahidadia. "Machine Learning." In Scientific Data Mining and Knowledge Discovery, 7–52. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02788-8_2.
Full textCios, Krzysztof J., Witold Pedrycz, and Roman W. Swiniarski. "Machine Learning." In Data Mining Methods for Knowledge Discovery, 229–308. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5589-6_6.
Full textBéjar, Javier. "Improving Knowledge Discovery Using Domain Knowledge in Unsupervised Learning." In Machine Learning: ECML 2000, 47–54. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-45164-1_6.
Full textŻytkow, Jan M. "Creating a Discoverer: Autonomous Knowledge Seeking Agent." In Machine Discovery, 253–83. Dordrecht: Springer Netherlands, 1997. http://dx.doi.org/10.1007/978-94-017-2124-0_5.
Full textConference papers on the topic "The Knowledge Machine"
Tahir, Ghulam Rasool, Sohail Asghar, and Nayyer Masood. "Knowledge Based Machine Translation." In 2010 International Conference on Information and Emerging Technologies (ICIET). IEEE, 2010. http://dx.doi.org/10.1109/iciet.2010.5625695.
Full textDunias, Par, Milan Hanajik, and N. Kouwenberg. "Knowledge-based machine vision." In Spatial Information from Digital Photogrammetry and Computer Vision: ISPRS Commission III Symposium, edited by Heinrich Ebner, Christian Heipke, and Konrad Eder. SPIE, 1994. http://dx.doi.org/10.1117/12.182828.
Full textHao, Xing-Wei, Xiang-Xu Meng, and Xu Cui. "A Knowledge Categorization Based Knowledge Ontology Metadata Model." In 2007 International Conference on Machine Learning and Cybernetics. IEEE, 2007. http://dx.doi.org/10.1109/icmlc.2007.4370864.
Full text"Knowledge Distillation for Machine Translation." In 2018 2nd International Conference on Computer Science and Intelligent Communication. Clausius Scientific Press, 2018. http://dx.doi.org/10.23977/csic.2018.0933.
Full textBenker, H., G. Watzlawik, J. M. Beacco, M. Dorochevsky, Th Jeffré, A. Pöhlmann, J. Noyé, B. Poterie, J. C. Syre, and O. Thibault. "KCM: a knowledge crunching machine." In the 16th annual international symposium. New York, New York, USA: ACM Press, 1989. http://dx.doi.org/10.1145/74925.74947.
Full textNirenburg, Sergei, Victor Raskin, and Allen Tucker. "On knowledge-based machine translation." In the 11th coference. Morristown, NJ, USA: Association for Computational Linguistics, 1986. http://dx.doi.org/10.3115/991365.991549.
Full textGong, Shaogang, and Hilary Buxton. "From Contextual Knowledge to Computational Constraints." In British Machine Vision Conference 1993. British Machine Vision Association, 1993. http://dx.doi.org/10.5244/c.7.23.
Full textYang, Yi, Zhenhua Wang, and Fuchao Wu. "Exploring Prior Knowledge for Pedestrian Detection." In British Machine Vision Conference 2015. British Machine Vision Association, 2015. http://dx.doi.org/10.5244/c.29.176.
Full textYongYue, Chen, and Xia HuoSong. "Research on Knowledge Extraction and Visualization in Knowledge Retrieve." In 2009 International Conference on Intelligent Human-Machine Systems and Cybernetics. IEEE, 2009. http://dx.doi.org/10.1109/ihmsc.2009.142.
Full textTock, D., I. Craw, and R. Lishman. "A knowledge based system for measuring faces." In British Machine Vision Conference 1990. British Machine Vision Association, 1990. http://dx.doi.org/10.5244/c.4.71.
Full textReports on the topic "The Knowledge Machine"
OREGON STATE UNIV CORVALLIS. Machine Learning for the Knowledge Plane. Fort Belvoir, VA: Defense Technical Information Center, June 2006. http://dx.doi.org/10.21236/ada454287.
Full textCarlson, Lynn, Elizabeth Cooper, Ronald Dolan, and Steve J. Maiorano. Representing Text Meaning for Multilingual Knowledge-Based Machine Translation. Fort Belvoir, VA: Defense Technical Information Center, October 1994. http://dx.doi.org/10.21236/ada302333.
Full textKoh, James. Machine Translation: A Key to Information Supremacy and Knowledge-Based Operations. Fort Belvoir, VA: Defense Technical Information Center, April 2003. http://dx.doi.org/10.21236/ada414522.
Full textMayer, John H. Explanation-Based Knowledge Acquisition of Schemas in Practical Electronics: A Machine Learning Approach. Fort Belvoir, VA: Defense Technical Information Center, September 1990. http://dx.doi.org/10.21236/ada229122.
Full textDwivedi, Dipankar, Grey Nearing, Hoshin Gupta, Alden Sampson, Laura Condon, Benjamin Ruddell, Daniel Klotz, et al. Knowledge-Guided Machine Learning (KGML) Platform to Predict Integrated Water Cycle and Associated extremes. Office of Scientific and Technical Information (OSTI), April 2021. http://dx.doi.org/10.2172/1769733.
Full textBringsjord, Selmer, Konstantine Arkoudas, and Yingrui Yang. New Architectures, Algorithms And Designs That Lead To Implemented Machine Reasoning Over Knowledge In Epistemic And Deontic Formats, In The Service Of Advanced Wargaming. Fort Belvoir, VA: Defense Technical Information Center, August 2006. http://dx.doi.org/10.21236/ada456936.
Full textBednar, Amy. Topological data analysis : an overview. Engineer Research and Development Center (U.S.), June 2021. http://dx.doi.org/10.21079/11681/40943.
Full textCordeiro de Amorim, Renato. A survey on feature weighting based K-Means algorithms. Web of Open Science, December 2020. http://dx.doi.org/10.37686/ser.v1i2.79.
Full text