AbdulHussien, 2017 - Google Patents
Comparison of machine learning algorithms to classify web pagesAbdulHussien, 2017
View PDF- Document ID
- 16556785376191662984
- Author
- AbdulHussien A
- Publication year
- Publication venue
- International Journal of Advanced Computer Science and Applications
External Links
Snippet
The 'World Wide Web', or simply the web, represents one of the largest sources of information in the world. We can say that any topic we think about is probably finding it's on the web. Web information comes in different forms and types such as text documents …
- 238000010801 machine learning 0 title abstract description 8
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
- G06F17/30861—Retrieval from the Internet, e.g. browsers
- G06F17/30864—Retrieval from the Internet, e.g. browsers by querying, e.g. search engines or meta-search engines, crawling techniques, push systems
- G06F17/30867—Retrieval from the Internet, e.g. browsers by querying, e.g. search engines or meta-search engines, crawling techniques, push systems with filtering and personalisation
-
- 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
- G06F17/3061—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F17/30634—Querying
-
- 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
- G06F17/30707—Clustering or classification into predefined 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
- G06F17/30587—Details of specialised database models
- G06F17/30595—Relational databases
- G06F17/30598—Clustering or classification
-
- 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
- G06F17/30386—Retrieval requests
-
- 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
- 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
- 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
- G06N5/02—Knowledge representation
- G06N5/022—Knowledge engineering, knowledge acquisition
-
- 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
-
- 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
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Zhang et al. | Taxogen: Unsupervised topic taxonomy construction by adaptive term embedding and clustering | |
| Pan et al. | Tri-party deep network representation | |
| Wang et al. | Relational stacked denoising autoencoder for tag recommendation | |
| Jotheeswaran et al. | OPINION MINING USING DECISION TREE BASED FEATURE SELECTION THROUGH MANHATTAN HIERARCHICAL CLUSTER MEASURE. | |
| AbdulHussien | Comparison of machine learning algorithms to classify web pages | |
| Huang et al. | High performance query expansion using adaptive co-training | |
| Mehta et al. | Sentiment analysis of tweets using supervised learning algorithms | |
| Anitha et al. | A framework to reduce category proliferation in fuzzy ARTMAP classifiers adopted for image retrieval using differential evolution algorithm | |
| Strat et al. | Hierarchical late fusion for concept detection in videos | |
| Zhang et al. | Taxogen: Constructing topical concept taxonomy by adaptive term embedding and clustering | |
| Parwita et al. | Classification of mobile application reviews using word embedding and convolutional neural network | |
| Pandiaraj et al. | Sentiment analysis on newspaper article reviews: contribution towards improved rider optimization-based hybrid classifier | |
| Karaman et al. | A comparative analysis of svm, lstm and cnn-rnn models for the bbc news classification | |
| Bing et al. | Learning a unified embedding space of web search from large-scale query log | |
| Shakibian et al. | Multi-kernel one class link prediction in heterogeneous complex networks | |
| ElAlami | Unsupervised image retrieval framework based on rule base system | |
| Azzam et al. | A question routing technique using deep neural network for communities of question answering | |
| Mahalakshmi et al. | Collaborative text and image based information retrieval model using bilstm and residual networks | |
| Kumar et al. | Personalized web service recommendation through mishmash technique and deep learning model | |
| Mudigonda et al. | IDEAL: an inventive optimized deep ensemble augmented learning framework for opinion mining and sentiment analysis | |
| Srivastava et al. | Redundancy and coverage aware enriched dragonfly-FL single document summarization | |
| Kumar et al. | A Recommendation System & Their Performance Metrics using several ML Algorithms | |
| Prajapati et al. | Context dependency relation extraction using modified evolutionary algorithm based on web mining | |
| Xu et al. | Cross-media retrieval based on pseudo-label learning and semantic consistency algorithm | |
| Bounabi et al. | Neural embedding & hybrid ml models for text classification |