Suchergebnisse - nonnumerical algorithms and problems − pattern machine

Ambiguous configurations for the 1D structure and motion problem

Autoren: Kalle Åström Fredrik Kahl

Quelle: Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001. 1:184-189

Schlagwörter: Pattern recognition, speech recognition, critical configuration, 0211 other engineering and technologies, 0202 electrical engineering, electronic engineering, information engineering, Cremona transformation, structure and motion, 02 engineering and technology, 0101 mathematics, Nonnumerical algorithms, 01 natural sciences, Machine vision and scene understanding

Dateibeschreibung: application/xml

Zugangs-URL: http://www.maths.lth.se/vision/publdb/reports/pdf/kahl-astrom-8iccv-01.pdf
https://link.springer.com/article/10.1023/A:1022120702016
https://www.lunduniversity.lu.se/lup/publication/794139fc-57fc-4fed-8440-033327d1e87b
https://dblp.uni-trier.de/db/journals/jmiv/jmiv18.html#AstromK03
https://portal.research.lu.se/en/publications/ambiguous-configurations-for-the-1d-structure-and-motion-problem-2
https://lup.lub.lu.se/search/publication/794139fc-57fc-4fed-8440-033327d1e87b
https://www.computer.org/csdl/proceedings-article/iccv/2001/114310184/12OmNyRg4nL
https://ieeexplore.ieee.org/document/937516/
http://www2.maths.lth.se/vision/publdb/reports/pdf/kahl-astrom-8iccv-01.pdf
https://www.lunduniversity.lu.se/lup/publication/c22943f6-2263-4bf2-a711-c0493a01e79f
https://lup.lub.lu.se/record/787350
http://dblp.uni-trier.de/db/conf/iccv/iccv2001-1.html#KahlA01

Classical machine learning and artificial neural network (ANN) to predict rejection in weaving industry.

Autoren: Ahmed, Toufique

Quelle: Journal of Electrical Systems & Information Technology; 6/24/2025, Vol. 12 Issue 1, p1-26, 26p

Schlagwörter: MACHINE learning, ARTIFICIAL neural networks, WEAVING, MULTIVARIATE analysis, TEXTILE industry, RANDOM forest algorithms, BOOSTING algorithms

A new maximum margin algorithm for one-class problems and its boosting implementation

Autoren: Jue Wang Qing Tao Gaowei Wu

Quelle: Pattern Recognition. 38:1071-1077

Schlagwörter: Statistical learning theory, Support vector machines, 4. Education, One-class problems, Learning and adaptive systems in artificial intelligence, 0202 electrical engineering, electronic engineering, information engineering, Outliers, 02 engineering and technology, Nonnumerical algorithms, Margin, Boosting

Dateibeschreibung: application/xml

Zugangs-URL: https://dblp.uni-trier.de/db/journals/pr/pr38.html#TaoWW05
http://www.irgrid.ac.cn/handle/1471x/1005602
https://dl.acm.org/doi/10.1016/j.patcog.2004.10.010
http://dblp.uni-trier.de/db/journals/pr/pr38.html#TaoWW05

Minimal and Hyper-Minimal Biautomata

Autoren: Holzer, Markus Jakobi, Sebastian Justus Liebig University Giessen et al.

Weitere Verfasser: Holzer, Markus Jakobi, Sebastian Justus Liebig University Giessen et al.

Quelle: Lecture Notes in Computer Science ISBN: 9783319096971

Schlagwörter: ddc:004, Data processing Computer science, Models of Computation (F.4.1), Unbounded-action devices (e.g., cellular automata, circuits, networks of machines), Classes defined by grammars or automata (e.g., context-free languages, regular sets, recursive sets), 0102 computer and information sciences, 02 engineering and technology, Routing and layout, 01 natural sciences, Classes defined by resource-bounded automata, Algebraic language theory, Self-modifying machines (e.g., neural networks), Relations among complexity classes, 0202 electrical engineering, electronic engineering, information engineering, Decision problems, Pattern matching, Complexity of proof procedures, Bounded-action devices (e.g., Turing machines, random access machines), Machine-independent complexity, computational complexity, Automata (e.g., finite, push-down, resource-bounded), Formal Languages (D.3.1), almost-equivalence, 4. Education, Relations between models, hyper-minimization, Computability theory, 16. Peace & justice, Reducibility and completeness, Geometrical problems and computations, Relations among complexity measures, Operations on languages, Computations on discrete structures, Sorting and searching, Sequencing and scheduling, biautomata, Complexity hierarchies, Complexity Measures and Classes (F.2) (REVISED), Nonnumerical Algorithms and Problems (E.2-5, G.2, H.2-3)

Dateibeschreibung: application/pdf

Zugangs-URL: http://geb.uni-giessen.de/geb/volltexte/2014/11056/pdf/Ifig_Report1401.pdf
http://www.informatik.uni-giessen.de/reports/Report1401.pdf
https://link.springer.com/chapter/10.1007/978-3-319-09698-8_26
https://rd.springer.com/chapter/10.1007/978-3-319-09698-8_26
http://geb.uni-giessen.de/geb/volltexte/2014/11056/

Differentially Private Kernel Support Vector Machines Based on the Exponential and Laplace Hybrid Mechanism.

Autoren: Sun, Zhenlong Yang, Jing Li, Xiaoye et al.

Quelle: Security & Communication Networks; 12/10/2021, p1-19, 19p

Schlagwörter: SUPPORT vector machines, CLASSIFICATION algorithms, MEAN value theorems, PROBLEM solving, ALGORITHMS, SYMMETRIC matrices, MACHINE learning

A taxonomy of unsupervised feature selection methods including their pros, cons, and challenges.

Autoren: Dwivedi, Rajesh Tiwari, Aruna Bharill, Neha et al.

Quelle: Journal of Supercomputing; Nov2024, Vol. 80 Issue 16, p24212-24240, 29p

Schlagwörter: PATTERN recognition systems, FEATURE selection, SCIENTIFIC literature, MACHINE learning, DATA mining

Machine learning models on the rigidity of welded shear tab connections.

Autoren: Farivar, Behzad Ghassemi, Babak Yousefian, Kaveh et al.

Quelle: Steel Construction: Design & Research; Feb2025, Vol. 18 Issue 1, p14-26, 13p

Schlagwörter: MACHINE learning, SUPPORT vector machines, REGRESSION analysis, CLASSIFICATION algorithms, SHEAR reinforcements, GEOMETRIC rigidity, FINITE element method, WELDED joints

Enhancing cloud security: A study on ensemble learning‐based intrusion detection systems.

Autoren: Al‐Sharif, Maha Bushnag, Anas

Quelle: IET Communications (Wiley-Blackwell); Oct2024, Vol. 18 Issue 16, p950-965, 16p

Schlagwörter: COMPUTER network security, MACHINE learning, ARTIFICIAL intelligence, RANDOM forest algorithms, FEATURE selection, INTRUSION detection systems (Computer security)

Using Factor Decomposition Machine Learning Method to Music Recommendation.

Autoren: Sun, Dapeng

Quelle: Complexity; 5/3/2021, p1-10, 10p

Schlagwörter: MACHINE learning, DATA conversion, DATA mining, DYNAMIC models, PROBLEM solving

Quadtree-based genetic algorithm and its applications to computer vision

Autoren: Yee-Hong Yang Minglun Gong

Quelle: Pattern Recognition. 37:1723-1733

Schlagwörter: Optimization, Image segmentation, Genetic algorithm, Learning and adaptive systems in artificial intelligence, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, Computing methodologies for image processing, Ill-posed problems, Motion estimation, Nonnumerical algorithms, Stereo vision, Machine vision and scene understanding, Quad-tree

Dateibeschreibung: application/xml

Machine Search Problem for Mathematical Expression.

Autoren: Diveev, A. I. Sofronova, E. A.

Quelle: Doklady Mathematics; 2024 Suppl 1, Vol. 110, pS25-S34, 10p

Schlagwörter: GENETIC programming, PROBLEM-based learning, ARTIFICIAL intelligence, COMPUTATIONAL mathematics, EVOLUTIONARY computation

Advancements in Soil Quality Assessment: A Comprehensive Review of Machine Learning and AI-Driven Approaches for Nutrient Deficiency Analysis.

Autoren: Barathkumar, S. Sellamuthu, K.M. Sathyabama, K. et al.

Quelle: Communications in Soil Science & Plant Analysis; 2025, Vol. 56 Issue 2, p257-282, 26p

Schlagwörter: SOIL management, ARTIFICIAL intelligence, DEEP learning, SOIL quality, DEFICIENCY diseases

Minimization, characterizations, and nondeterminism for biautomata

Autoren: Holzer, Markus Jakobi, Sebastian Justus Liebig University Giessen et al.

Weitere Verfasser: Holzer, Markus Jakobi, Sebastian Justus Liebig University Giessen et al.

Schlagwörter: ddc:004, Data processing Computer science, cyclic languages, commutative languages, Routing and layout, Parallel rewriting systems (e.g., developmental systems, L-systems), Grammars and Other Rewriting Systems (D.3.1), Grammar types (e.g., context-free, context-sensitive), Relations among complexity classes, Decision problems, Pattern matching, Thue systems, Complexity of proof procedures, Parsing, Machine-independent complexity, Brzozowski´s minimization algorithm, deterministic and nondeterministic biautomata, Reducibility and completeness, Geometrical problems and computations, Relations among complexity measures, Computations on discrete structures, Sorting and searching, Sequencing and scheduling, Complexity hierarchies, Complexity Measures and Classes (F.2) (REVISED), Nonnumerical Algorithms and Problems (E.2-5, G.2, H.2-3)

Dateibeschreibung: application/pdf

Zugangs-URL: http://geb.uni-giessen.de/geb/volltexte/2014/11054/

Surrogate Model-Driven Estimation of Adiabatic Surface Temperature of Fire Exposed Suspension Bridge Towers: Surrogate Model-Driven Estimation of Adiabatic.

Autoren: Mostofi, Sara Altunişik, Ahmet Can

Quelle: Fire Technology; Mar2025, Vol. 61 Issue 2, p603-630, 28p

Schlagwörter: MACHINE learning, ADIABATIC temperature, SURFACE temperature, RANDOM forest algorithms, RIFLE-ranges

Performance Analysis of SVM and Linear Regression for Predicting Tourist Visits in North Sumatera.

Autoren: Ginting, Andriyan Nurdin Agusniar, Cut

Quelle: International Journal of Engineering Science & Information Technology; 2025, Vol. 5 Issue 1, p101-108, 8p

Schlagwörter: TOURIST attractions, TOURISM, SUPPORT vector machines, CULTURAL pluralism, GROSS domestic product

Machine learning and IoTs for forecasting prediction of smart road traffic flow.

Autoren: Chuanxia, Sun Han, Zhang Peixuan, Yin

Quelle: Soft Computing - A Fusion of Foundations, Methodologies & Applications; Jan2023, Vol. 27 Issue 1, p323-335, 13p

Schlagwörter: DEEP learning, TRAFFIC flow, MACHINE learning, RANDOM forest algorithms, TRAFFIC accidents, LOGISTIC regression analysis, PREDICTION models

Enhancing context awareness in augmented reality environments: a review of opportunities and challenges in object and action recognition through machine learning.

Autoren: Chamusca, Iolanda L. Pagano, Tiago P. Loureiro, Rafael B. et al.

Quelle: Discover Applied Sciences; Jul2025, Vol. 7 Issue 7, p1-18, 18p

Evaluating the Machine Learning Literature: A Primer and User's Guide for Psychiatrists.

Autoren: Grzenda, Adrienne Kraguljac, Nina V. McDonald, William M. et al.

Quelle: Focus: The Journal of Lifelong Learning in Psychiatry; Spring2025, Vol. 23 Issue 2, p270-284, 15p

Artificial Intelligence and Machine Learning Applications in Critically Ill Brain Injured Patients.

Autoren: Vitt, Jeffrey R. Mainali, Shraddha

Quelle: Seminars in Neurology; Jun2024, Vol. 44 Issue 3, p342-356, 15p

Schlagwörter: ARTIFICIAL intelligence, MACHINE learning, MEDICAL personnel, CRITICALLY ill, DATA privacy

Faster Fully Compressed Pattern Matching by Recompression.

Autoren: JEŻ, ARTUR

Quelle: ACM Transactions on Algorithms; Jan2015, Vol. 11 Issue 3, p20:1-20:43, 43p

Schlagwörter: STRAIGHT-line mechanisms, PATTERN matching, LEMPEL-Ziv algorithm, MACHINE learning, ALGORITHMS