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Статті в журналах з теми "170203 Knowledge Representation and Machine Learning"
Twine, S. "Knowledge representation and organization in machine learning." Information and Software Technology 32, no. 7 (September 1990): 510–11. http://dx.doi.org/10.1016/0950-5849(90)90171-m.
Повний текст джерелаMaher, Mary Lou, and Heng Li. "Learning design concepts using machine learning techniques." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 8, no. 2 (1994): 95–111. http://dx.doi.org/10.1017/s0890060400000706.
Повний текст джерелаMa, Yunpu, and Volker Tresp. "Quantum Machine Learning Algorithm for Knowledge Graphs." ACM Transactions on Quantum Computing 2, no. 3 (September 30, 2021): 1–28. http://dx.doi.org/10.1145/3467982.
Повний текст джерелаMaher, Mary Lou, David C. Brown, and Alex Duffy. "Special Issue: Machine Learning in Design." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 8, no. 2 (1994): 81–82. http://dx.doi.org/10.1017/s0890060400000688.
Повний текст джерелаLittman, David, and Maarten van Someren. "International Workshop on Knowledge Representation and Organization in Machine Learning." AI Communications 1, no. 1 (1988): 44–45. http://dx.doi.org/10.3233/aic-1988-1108.
Повний текст джерелаRobinson, Peter N., and Melissa A. Haendel. "Ontologies, Knowledge Representation, and Machine Learning for Translational Research: Recent Contributions." Yearbook of Medical Informatics 29, no. 01 (August 2020): 159–62. http://dx.doi.org/10.1055/s-0040-1701991.
Повний текст джерелаMoreno, Marcio, Vítor Lourenço, Sandro Rama Fiorini, Polyana Costa, Rafael Brandão, Daniel Civitarese, and Renato Cerqueira. "Managing Machine Learning Workflow Components." International Journal of Semantic Computing 14, no. 02 (June 2020): 295–309. http://dx.doi.org/10.1142/s1793351x20400115.
Повний текст джерелаUllah, AMM Sharif. "Fundamental Issues of Concept Mapping Relevant to Discipline-Based Education: A Perspective of Manufacturing Engineering." Education Sciences 9, no. 3 (August 29, 2019): 228. http://dx.doi.org/10.3390/educsci9030228.
Повний текст джерелаGRANER, NICOLAS, SUNIL SHARMA, D. SLEEMAN, MICHALIS RISSAKIS, SUSAN CRAW, and CHRIS MOORE. "THE MACHINE LEARNING TOOLBOX CONSULTANT." International Journal on Artificial Intelligence Tools 02, no. 03 (September 1993): 307–28. http://dx.doi.org/10.1142/s0218213093000163.
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 "170203 Knowledge Representation and Machine Learning"
Leitner, Jürgen. "From vision to actions: Towards adaptive and autonomous humanoid robots." Thesis, Università della Svizzera Italiana, 2014. https://eprints.qut.edu.au/90178/2/2014INFO020.pdf.
Повний текст джерелаAlirezaie, Marjan. "Semantic Analysis Of Multi Meaning Words Using Machine Learning And Knowledge Representation." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-70086.
Повний текст джерелаTuovinen, L. (Lauri). "From machine learning to learning with machines:remodeling the knowledge discovery process." Doctoral thesis, Oulun yliopisto, 2014. http://urn.fi/urn:isbn:9789526205243.
Повний текст джерелаTiivistelmä Tiedonlouhintateknologialla etsitään automoidusti tietoa suurista määristä digitaalista dataa. Vakiintunut prosessimalli kuvaa tiedonlouhintaprosessia lineaarisesti ja teknologiakeskeisesti sarjana muunnoksia, jotka jalostavat raakadataa yhä abstraktimpiin ja tiivistetympiin esitysmuotoihin. Todellisissa tiedonlouhintaprosesseissa on kuitenkin aina osa-alueita, joita tällainen malli ei kata riittävän hyvin. Erityisesti on huomattava, että eräät prosessin tärkeimmistä toimijoista ovat ihmisiä, eivät teknologiaa, ja että heidän toimintansa prosessissa on luonteeltaan vuorovaikutteista eikä sarjallista. Tässä väitöskirjassa ehdotetaan vakiintuneen mallin täydentämistä siten, että tämä tiedonlouhintaprosessin laiminlyöty ulottuvuus otetaan huomioon. Ehdotettu prosessimalli koostuu kolmesta osamallista, jotka ovat tietomalli, työnkulkumalli ja arkkitehtuurimalli. Kukin osamalli tarkastelee tiedonlouhintaprosessia eri näkökulmasta: tietomallin näkökulma käsittää tiedon eri olomuodot sekä muunnokset olomuotojen välillä, työnkulkumalli kuvaa prosessin toimijat sekä niiden väliset vuorovaikutukset, ja arkkitehtuurimalli ohjaa prosessin suorittamista tukevien ohjelmistojen suunnittelua. Väitöskirjassa määritellään aluksi kullekin osamallille joukko vaatimuksia, minkä jälkeen esitetään vaatimusten täyttämiseksi suunniteltu ratkaisu. Lopuksi palataan tarkastelemaan vaatimuksia ja osoitetaan, kuinka ne on otettu ratkaisussa huomioon. Väitöskirjan pääasiallinen kontribuutio on se, että se avaa tiedonlouhintaprosessiin valtavirran käsityksiä laajemman tarkastelukulman. Väitöskirjan sisältämä täydennetty prosessimalli hyödyntää vakiintunutta mallia, mutta laajentaa sitä kokoamalla tiedonhallinnan ja tietämyksen esittämisen, tiedon louhinnan työnkulun sekä ohjelmistoarkkitehtuurin osatekijöiksi yhdistettyyn malliin. Lisäksi malli kattaa aiheita, joita tavallisesti ei oteta huomioon tai joiden ei katsota kuuluvan osaksi tiedonlouhintaprosessia; tällaisia ovat esimerkiksi tiedon louhintaan liittyvät filosofiset kysymykset. Väitöskirjassa käsitellään myös kahta ohjelmistokehystä ja neljää tapaustutkimuksena esiteltävää sovellusta, jotka edustavat teknisiä ratkaisuja eräisiin yksittäisiin tiedonlouhintaprosessin osaongelmiin. Kehykset ja sovellukset toteuttavat ja havainnollistavat useita ehdotetun prosessimallin merkittävimpiä ominaisuuksia
Duminy, Willem H. "A learning framework for zero-knowledge game playing agents." Pretoria : [s.n.], 2006. http://upetd.up.ac.za/thesis/available/etd-10172007-153836.
Повний текст джерелаOramas, Martín Sergio. "Knowledge extraction and representation learning for music recommendation and classification." Doctoral thesis, Universitat Pompeu Fabra, 2017. http://hdl.handle.net/10803/457709.
Повний текст джерелаEn esta tesis, abordamos los problemas de clasificar y recomendar música en grandes colecciones, centrándonos en el enriquecimiento semántico de descripciones (biografías, reseñas, metadatos), y en el aprovechamiento de datos multimodales (textos, audios e imágenes). Primero nos centramos en enlazar textos con bases de conocimiento y en su construcción automatizada. Luego mostramos cómo el modelado de información semántica puede impactar en estudios musicológicos, y contribuye a superar a métodos basados en texto, tanto en similitud como en clasificación y recomendación de música. A continuación, investigamos el aprendizaje de nuevas representaciones de datos a partir de contenidos multimodales utilizando redes neuronales, y lo aplicamos a los problemas de recomendar música nueva y clasificar géneros musicales con múltiples etiquetas, mostrando que el enriquecimiento semántico y la combinación de representaciones aprendidas produce mejores resultados.
Choi, Jin-Woo. "Action Recognition with Knowledge Transfer." Diss., Virginia Tech, 2021. http://hdl.handle.net/10919/101780.
Повний текст джерелаDoctor of Philosophy
Recent progress on deep learning has shown remarkable action recognition performance. The remarkable performance is often achieved by transferring the knowledge learned from existing large-scale data to the small-scale data specific to applications. However, existing action recog- nition models do not always work well on new tasks and datasets because of the following two problems. i) Current action recognition datasets have a spurious correlation between action types and background scene types. The models trained on these datasets are biased towards the scene instead of focusing on the actual action. This scene bias leads to poor performance on the new datasets and tasks. ii) Directly testing the model trained on the source data on the target data leads to poor performance as the source, and target distributions are different. Fine-tuning the model on the target data can mitigate this issue. However, manual labeling small-scale target videos is labor-intensive. In this dissertation, I propose solutions to these two problems. To tackle the first problem, I propose to learn scene-invariant action representations to mitigate background scene- biased human action recognition models for the first problem. Specifically, the proposed method learns representations that cannot predict the scene types and the correct actions when there is no evidence. I validate the proposed method's effectiveness by transferring the pre-trained model to multiple action understanding tasks. The results show consistent improvement over the baselines for every task and dataset. To handle the second problem, I formulate human action recognition as an unsupervised learning problem on the target data. In this setting, we have many labeled videos as source data and unlabeled videos as target data. We can use already existing labeled video datasets as source data in this setting. The task is to align the source and target feature distributions so that the learned model can generalize well on the target data. I propose 1) aligning the more important temporal part of each video and 2) encouraging the model to focus on action, not the background scene. The proposed method is simple and intuitive while achieving state-of-the-art performance without training on a lot of labeled target videos. I relax the unsupervised target data setting to a sparsely labeled target data setting. Here, we have many labeled videos as source data and sparsely labeled videos as target data. The setting is practical as sometimes we can afford a little bit of cost for labeling target data. I propose multiple video data augmentation methods to inject color, spatial, temporal, and scene invariances to the action recognition model in this setting. The resulting method shows favorable performance on the public benchmarks.
Sudre, Gustavo. "Characterizing the Spatiotemporal Neural Representation of Concrete Nouns Across Paradigms." Research Showcase @ CMU, 2012. http://repository.cmu.edu/dissertations/315.
Повний текст джерелаDuminy, Willem Harklaas. "A learning framework for zero-knowledge game playing agents." Diss., University of Pretoria, 2007. http://hdl.handle.net/2263/28767.
Повний текст джерелаDissertation (MSc)--University of Pretoria, 2007.
Computer Science
MSc
Unrestricted
Jones, Joshua K. "Empirically-based self-diagnosis and repair of domain knowledge." Diss., Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/33931.
Повний текст джерелаBulgarov, Florin Adrian. "Toward Supporting Fine-Grained, Structured, Meaningful and Engaging Feedback in Educational Applications." Thesis, University of North Texas, 2018. https://digital.library.unt.edu/ark:/67531/metadc1404562/.
Повний текст джерелаКниги з теми "170203 Knowledge Representation and Machine Learning"
Morik, Katharina, ed. Knowledge Representation and Organization in Machine Learning. Berlin, Heidelberg: Springer Berlin Heidelberg, 1989. http://dx.doi.org/10.1007/bfb0017213.
Повний текст джерелаKatharina, Morik, ed. Knowledge representation and organization in machine learning. Berlin: Springer-Verlag, 1989.
Знайти повний текст джерелаKumar, Avadhesh, Shrddha Sagar, T. Ganesh Kumar, and K. Sampath Kumar. Prediction and Analysis for Knowledge Representation and Machine Learning. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003126898.
Повний текст джерелаMachine learning of robot assembly plans. Boston: Kluwer Academic Publishers, 1988.
Знайти повний текст джерелаEmde, Werner. Modellbildung, Wissensrevision und Wissensrepräsentation im Maschinellen Lernen. Berlin: Springer, 1991.
Знайти повний текст джерелаPacific, Rim International Conference on Artificial Intelligence (4th 1996 Cairns Qld ). PRICAI '96: Topics in artificial intelligence : 4th Pacific Rim International Conference on Artificial Intelligence, Cairns, Australia, August 26-30, 1996 : proceedings. Berlin: Springer, 1996.
Знайти повний текст джерела1953-, Benjamin D. Paul, ed. Change of representation and inductive bias. Boston: Kluwer Academic, 1990.
Знайти повний текст джерелаMotta, E. Reusable components for knowledge modelling: Case studies in parametric design problem solving. Amsterdam: IOS Press, 2000.
Знайти повний текст джерелаG, Antoniou, Ghose Aditya K, Truszczyński Mirosław, Workshop on Inducing Complex Representations (1996 : Cairns, Qld.), and Pacific Rim International Conference on Artificial Intelligence (4th : 1996 : Cairns, Qld.), eds. Learning and reasoning with complex representations: PRICAI'96 Workshops on Reasoning with Incomplete and Changing Information and on Inducing Complex Representations, Cairns, Australia, August 26-30, 1996 : selected papers. Berlin: Springer, 1998.
Знайти повний текст джерелаInternational Conference on Knowledge Modeling & Expertise Transfer (1st 1991 Sophia-Antipolis, France). Knowledge modeling & expertise transfer: Proceedings of the first International Conference on Knowledge Modeling & Expertise Transfer, Sophia-Antipolis, French Riviera, France, April 22-24, 1991. Amsterdam: IOS Press, 1991.
Знайти повний текст джерелаЧастини книг з теми "170203 Knowledge Representation and Machine Learning"
Neri, Filippo, and Lorenza Saitta. "Knowledge representation in machine learning." In Machine Learning: ECML-94, 20–27. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/3-540-57868-4_48.
Повний текст джерелаSowmyayani, S. "Machine Learning." In Prediction and Analysis for Knowledge Representation and Machine Learning, 1–31. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003126898-1.
Повний текст джерелаMuthu Lakshmi, G., and N. Krishnammal. "Multi-View Representation Learning." In Prediction and Analysis for Knowledge Representation and Machine Learning, 175–98. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003126898-9.
Повний текст джерелаCiucci, Davide, Stefania Boffa, and Andrea Campagner. "Orthopartitions in Knowledge Representation and Machine Learning." In Rough Sets, 3–18. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-21244-4_1.
Повний текст джерелаKhosla, Megha, Jurek Leonhardt, Wolfgang Nejdl, and Avishek Anand. "Node Representation Learning for Directed Graphs." In Machine Learning and Knowledge Discovery in Databases, 395–411. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-46150-8_24.
Повний текст джерелаLuo, Dijun, Feiping Nie, Chris Ding, and Heng Huang. "Multi-Subspace Representation and Discovery." In Machine Learning and Knowledge Discovery in Databases, 405–20. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23783-6_26.
Повний текст джерелаPapreja, Piyush, Hemanth Venkateswara, and Sethuraman Panchanathan. "Representation, Exploration and Recommendation of Playlists." In Machine Learning and Knowledge Discovery in Databases, 543–50. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-43887-6_50.
Повний текст джерелаLuo, Peng, Jinye Peng, Ziyu Guan, and Jianping Fan. "Multi-view Semantic Learning for Data Representation." In Machine Learning and Knowledge Discovery in Databases, 367–82. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23528-8_23.
Повний текст джерелаvan Someren, Maarten W. "Using attribute dependencies for rule learning." In Knowledge Representation and Organization in Machine Learning, 192–210. Berlin, Heidelberg: Springer Berlin Heidelberg, 1989. http://dx.doi.org/10.1007/bfb0017223.
Повний текст джерелаLi, Xin, and Yuhong Guo. "Bi-directional Representation Learning for Multi-label Classification." In Machine Learning and Knowledge Discovery in Databases, 209–24. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-44851-9_14.
Повний текст джерелаТези доповідей конференцій з теми "170203 Knowledge Representation and Machine Learning"
López, Beatriz, Natàlia Mordvanyuk, Pablo Gay, and Albert Pla. "Knowledge representation and machine learning on wearable sensor data." In DATA '18: International Conference on Data Science, E-learning and Information Systems 2018. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3279996.3280041.
Повний текст джерелаMartínez-Rojas, Antonio, Andrés Jiménez-Ramírez, and Jose Enríquez. "Towards a Unified Model Representation of Machine Learning Knowledge." In 4th International Special Session on Advanced practices in Model-Driven Web Engineering. SCITEPRESS - Science and Technology Publications, 2019. http://dx.doi.org/10.5220/0008559204700476.
Повний текст джерелаMartínez-Rojas, Antonio, Andrés Jiménez-Ramírez, and Jose Enríquez. "Towards a Unified Model Representation of Machine Learning Knowledge." In 4th International Special Session on Advanced practices in Model-Driven Web Engineering. SCITEPRESS - Science and Technology Publications, 2019. http://dx.doi.org/10.5220/0008559200002366.
Повний текст джерелаChen, Wenrui, Chuyao Luo, Shaokai Wang, and Yunming Ye. "Representation learning with complete semantic description of knowledge graphs." In 2017 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2017. http://dx.doi.org/10.1109/icmlc.2017.8107756.
Повний текст джерелаMing-Hu Ha, Yan Li, Hai-Jun Li, and Peng Wang. "A new form of knowledge representation and reasoning." In Proceedings of 2005 International Conference on Machine Learning and Cybernetics. IEEE, 2005. http://dx.doi.org/10.1109/icmlc.2005.1527378.
Повний текст джерелаKhummongkol, Rojanee, and Masao Yokota. "Systematic representation and computation of human intuitive spatiotemporal knowledge as mental imagery." In 2016 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2016. http://dx.doi.org/10.1109/icmlc.2016.7873002.
Повний текст джерелаSabbatini, Federico, and Roberta Calegari. "Symbolic Knowledge Extraction from Opaque Machine Learning Predictors: GridREx & PEDRO." In 19th International Conference on Principles of Knowledge Representation and Reasoning {KR-2022}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/kr.2022/57.
Повний текст джерелаAbd, Maan Tareq, Masnizah Mohd, and Mustafa Tareq Abd. "Investigation of Data Representation Methods with Machine Learning Algorithms for Biomedical Named Enttity Recognition." In 2018 Fourth International Conference on Information Retrieval and Knowledge Management (CAMP). IEEE, 2018. http://dx.doi.org/10.1109/infrkm.2018.8464816.
Повний текст джерелаHeng Chung, Matthew Wai, Jianyu Liu, and Hegler Tissot. "Clinical Knowledge Graph Embedding Representation Bridging the Gap between Electronic Health Records and Prediction Models." In 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA). IEEE, 2019. http://dx.doi.org/10.1109/icmla.2019.00237.
Повний текст джерелаLand, Jr., Walker H., Mark J. Embrechts, Frances R. Anderson, Tom Smith, Lut Wong, Steve Fahlbusch, and Robert Choma. "Integrating knowledge representation/engineering, the multivariant PNN, and machine learning to improve breast cancer diagnosis." In Defense and Security, edited by Belur V. Dasarathy. SPIE, 2005. http://dx.doi.org/10.1117/12.604575.
Повний текст джерела