[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Pan et al., 2023 - Google Patents

A machine learning based approach to detect machine learning design patterns

Pan et al., 2023

View PDF
Document ID
7039054511560546577
Author
Pan W
Washizaki H
Yoshioka N
Fukazawa Y
Khomh F
Guéhéneuc Y
Publication year
Publication venue
2023 30th Asia-Pacific Software Engineering Conference (APSEC)

External Links

Snippet

As machine learning expands to various domains, the demand for reusable solutions to similar problems increases. Machine learning design patterns are reusable solutions to design problems of machine learning applications. They can significantly enhance …
Continue reading at drive.google.com (PDF) (other versions)

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
    • G06F17/20—Handling natural language data
    • G06F17/27—Automatic analysis, e.g. parsing
    • G06F17/2765—Recognition
    • 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
    • G06F17/27—Automatic analysis, e.g. parsing
    • G06F17/2705—Parsing
    • 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
    • G06F17/30634—Querying
    • 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
    • G06K9/6267—Classification techniques
    • G06K9/6279—Classification techniques relating to the number of classes
    • 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
    • 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
    • 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
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computer systems utilising knowledge based models
    • G06N5/04—Inference methods or devices
    • 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
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/36—Preventing errors by testing or debugging software
    • G06F11/3668—Software testing
    • 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

Similar Documents

Publication Publication Date Title
Lin et al. Traceability transformed: Generating more accurate links with pre-trained bert models
Rabin et al. Understanding neural code intelligence through program simplification
Yang et al. Aspect-based api review classification: How far can pre-trained transformer model go?
Rahman et al. A neural network based intelligent support model for program code completion
Steidl et al. Feature-based detection of bugs in clones
Del Carpio et al. Trends in software engineering processes using deep learning: a systematic literature review
Mastropaolo et al. An empirical study on code comment completion
Pan et al. Automatic noisy label correction for fine-grained entity typing
Huang et al. Code review knowledge perception: Fusing multi-features for salient-class location
Akli et al. FlakyCat: Predicting flaky tests categories using few-shot learning
Lan et al. BTLink: automatic link recovery between issues and commits based on pre-trained BERT model
Pan et al. A machine learning based approach to detect machine learning design patterns
North et al. Code gradients: Towards automated traceability of llm-generated code
CN117873558A (en) Self-recognition technology debt detection method and device based on size model fusion
Wickramasinghe et al. Smart exam evaluator for object-oriented programming modules
Peddavenkatagari EVALUATION METHODOLOGIES AND PERFORMANCE METRICS IN SUPERVISED NER FOR JOURNAL ARTICLES: A CRITICAL ANALYSIS
Sinpang et al. Detecting ambiguity in requirements analysis using mamdani fuzzy inference
Oli et al. Automated assessment of quality of Jupyter Notebooks using artificial intelligence and big code
Kapur et al. Using Paragraph Vectors to improve our existing code review assisting tool-CRUSO
Shahariar et al. Contrastive Learning for API Aspect Analysis
Qian et al. Semantic-based false alarm detection approach via machine learning
Izadi et al. An empirical study on data leakage and generalizability of link prediction models for issues and commits
Tarasconi et al. Natural Language processing applications in case-law text publishing
CN118377472B (en) Quick construction system based on code automatic generation
Huang et al. CIGAR: Contrastive Learning for GitHub Action Recommendation