Magdalena-Benedicto et al., 2023 - Google Patents
Challenges and opportunities in machine learning for geometryMagdalena-Benedicto et al., 2023
View HTML- Document ID
- 4299108113851101792
- Author
- Magdalena-Benedicto R
- Pérez-Díaz S
- Costa-Roig A
- Publication year
- Publication venue
- Mathematics
External Links
Snippet
Over the past few decades, the mathematical community has accumulated a significant amount of pure mathematical data, which has been analyzed through supervised, semi- supervised, and unsupervised machine learning techniques with remarkable results, eg …
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/30286—Information retrieval; Database structures therefor; File system structures therefor in structured data stores
- G06F17/30587—Details of specialised database models
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/20—Handling natural language data
- G06F17/21—Text processing
- G06F17/22—Manipulating or registering by use of codes, e.g. in sequence of text characters
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/20—Handling natural language data
- G06F17/21—Text processing
- G06F17/24—Editing, e.g. insert/delete
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/30908—Information retrieval; Database structures therefor; File system structures therefor of semistructured data, the undelying structure being taken into account, e.g. mark-up language structure data
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/3061—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for a specific business sector, e.g. utilities or tourism
- G06Q50/01—Social networking
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/02—Knowledge representation
- G06N5/022—Knowledge engineering, knowledge acquisition
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/10—Office automation, e.g. computer aided management of electronic mail or groupware; Time management, e.g. calendars, reminders, meetings or time accounting
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for a specific business sector, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/20—Education
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F15/00—Digital computers in general; Data processing equipment in general
- G06F15/18—Digital computers in general; Data processing equipment in general in which a programme is changed according to experience gained by the computer itself during a complete run; Learning machines
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N7/00—Computer systems based on specific mathematical models
- G06N7/005—Probabilistic networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Mukhamediev et al. | Review of artificial intelligence and machine learning technologies: classification, restrictions, opportunities and challenges | |
| Joloudari et al. | Effective class-imbalance learning based on SMOTE and convolutional neural networks | |
| Zhao et al. | Sentiment analysis based requirement evolution prediction | |
| Fu et al. | A novel method to determine basic probability assignment based on adaboost and its application in classification | |
| Gana et al. | Leveraging llms for efficient topic reviews | |
| Zhao et al. | Feature selection of grey wolf optimizer based on quantum computing and uncertain symmetry rough set | |
| Li et al. | A generative adversarial network structure for learning with small numerical data sets | |
| Bonora | Perturbative and non-pertrubative trace anomalies | |
| Ruiz-Casado et al. | Enhancing histopathological image classification performance through synthetic data generation with generative adversarial networks | |
| Wu et al. | Ricci curvature-based semi-supervised learning on an attributed network | |
| Wang et al. | The study of multiple classes boosting classification method based on local similarity | |
| Magdalena-Benedicto et al. | Challenges and opportunities in machine learning for geometry | |
| Yu et al. | Hybrid granularities transformer for fine-grained image recognition | |
| Safi et al. | An enhanced tree ensemble for classification in the presence of extreme class imbalance | |
| Zhang et al. | A novel hybrid high-dimensional pso clustering algorithm based on the cloud model and entropy | |
| Daskin et al. | Dimension reduction and redundancy removal through successive Schmidt decompositions | |
| Mendonça et al. | On the use of kullback–leibler divergence for kernel selection and interpretation in variational autoencoders for feature creation | |
| Kumar et al. | Finite State GUI Testing with Test Case Prioritization Using Z-BES and GK-GRU | |
| dos Santos et al. | RL-SSI Model: Adapting a Supervised Learning Approach to a Semi-Supervised Approach for Human Action Recognition | |
| Panagiotakis | Particle swarm optimization-based unconstrained polygonal fitting of 2D shapes | |
| Zamora et al. | An Ensemble and Multi-View Clustering Method Based on Kolmogorov Complexity | |
| Noreen et al. | Performance Evaluation of Convolutional Neural Network for Multi-Class in Cross Project Defect Prediction | |
| Yi et al. | SFS-AGGL: Semi-supervised feature selection integrating adaptive graph with global and local information | |
| Seol et al. | Analysis of Quantum-Classical Hybrid Deep Learning for 6G Image Processing with Copyright Detection | |
| Zou et al. | Imbalanced data parameter optimization of convolutional neural networks based on analysis of variance |