Tanha, 2019 - Google Patents
A multiclass boosting algorithm to labeled and unlabeled dataTanha, 2019
- Document ID
- 2587073606958510556
- Author
- Tanha J
- Publication year
- Publication venue
- International Journal of Machine Learning and Cybernetics
External Links
Snippet
In this article we focus on the semi-supervised learning. Semi-supervised learning typically is a learning task from both labeled and unlabeled data. We especially consider the multiclass semi-supervised classification problem. To solve the multiclass semi-supervised …
- 238000004422 calculation algorithm 0 title abstract description 61
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
- G06F17/3061—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F17/30705—Clustering or classification
- G06F17/3071—Clustering or classification including class or cluster creation or modification
-
- 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
- G06F17/30595—Relational databases
- G06F17/30598—Clustering or classification
- G06F17/30601—Clustering or classification including cluster or class visualization or browsing
-
- 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
- G06K9/6267—Classification techniques
- G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
- G06K9/627—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches based on distances between the pattern to be recognised and training or reference patterns
-
- 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
- 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
- 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
- G06F17/30312—Storage and indexing structures; Management thereof
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
-
- 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
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/04—Inference methods or devices
-
- 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
- G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
- G06K9/46—Extraction of features or characteristics of the image
-
- 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
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
-
- 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
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Zhang et al. | Research and application of XGBoost in imbalanced data | |
Lan et al. | A survey of data mining and deep learning in bioinformatics | |
Tiwari et al. | Towards a quantum-inspired binary classifier | |
Levatić et al. | Semi-supervised classification trees | |
Zemmal et al. | Particle swarm optimization based swarm intelligence for active learning improvement: Application on medical data classification | |
WO2020005240A1 (en) | Adapting a sequence model for use in predicting future device interactions with a computing system | |
Sun et al. | Online ensemble using adaptive windowing for data streams with concept drift | |
Tanha | A multiclass boosting algorithm to labeled and unlabeled data | |
AghaeiRad et al. | Improve credit scoring using transfer of learned knowledge from self-organizing map | |
Chopra et al. | Introduction to machine learning with Python | |
Arun et al. | A hybrid deep learning architecture for latent topic-based image retrieval | |
Gong et al. | A sparse reconstructive evidential k-nearest neighbor classifier for high-dimensional data | |
Guo et al. | Unsupervised discriminative feature representation via adversarial auto-encoder | |
Kayaalp et al. | Benchmarking the clustering performances of evolutionary algorithms: a case study on varying data size | |
Wu et al. | MapReduce-based adaptive random forest algorithm for multi-label classification | |
Ishfaq et al. | Empirical analysis of machine learning algorithms for multiclass prediction | |
Zhou et al. | Active learning of Gaussian processes with manifold-preserving graph reduction | |
Biju et al. | Friedman and Wilcoxon evaluations comparing SVM, bagging, boosting, K-NN and decision tree classifiers | |
Chemchem et al. | Deep learning and data mining classification through the intelligent agent reasoning | |
Tarawneh et al. | CTELC: a constant-time ensemble learning classifier based on KNN for big data | |
Mohammed et al. | Big data classification with optimization driven MapReduce framework | |
Liang et al. | Landmarking manifolds with Gaussian processes | |
Kanimozhi et al. | An intelligent incremental filtering feature selection and clustering algorithm for effective classification | |
Yu et al. | A total least squares proximal support vector classifier for credit risk evaluation | |
Xu et al. | Research on context-aware group recommendation based on deep learning |