Class information for: |
Basic class information |
Class id | #P | Avg. number of references |
Database coverage of references |
---|---|---|---|
25585 | 277 | 31.6 | 33% |
Hierarchy of classes |
The table includes all classes above and classes immediately below the current class. |
Cluster id | Level | Cluster label | #P |
---|---|---|---|
9 | 4 | COMPUTER SCIENCE, THEORY & METHODS//COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE//COMPUTER SCIENCE, INFORMATION SYSTEMS | 1247339 |
211 | 3 | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE//IEEE TRANSACTIONS ON NEURAL NETWORKS//DATA MINING | 51632 |
235 | 2 | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE//FEATURE SELECTION//MACHINE LEARNING | 20237 |
25585 | 1 | SEMI SUPERVISED LEARNING//CO TRAINING//TRI TRAINING | 277 |
Terms with highest relevance score |
rank | Term | termType | Chi square | Shr. of publ. in class containing term |
Class's shr. of term's tot. occurrences |
#P with term in class |
---|---|---|---|---|---|---|
1 | SEMI SUPERVISED LEARNING | authKW | 1071791 | 31% | 11% | 85 |
2 | CO TRAINING | authKW | 955341 | 9% | 33% | 26 |
3 | TRI TRAINING | authKW | 306206 | 2% | 56% | 5 |
4 | LABELED AND UNLABELED SAMPLES | authKW | 220471 | 1% | 100% | 2 |
5 | LEARNING FROM UNLABELED DATA | authKW | 220471 | 1% | 100% | 2 |
6 | SAFE MECHANISM | authKW | 220471 | 1% | 100% | 2 |
7 | TEMPORAL RBF | authKW | 220471 | 1% | 100% | 2 |
8 | UNLABELED DATA | authKW | 207639 | 3% | 21% | 9 |
9 | SEMISUPERVISED LEARNING | authKW | 175635 | 5% | 11% | 14 |
10 | SEMI SUPERVISED CLASSIFICATION | authKW | 150990 | 4% | 14% | 10 |
Web of Science journal categories |
Rank | Term | Chi square | Shr. of publ. in class containing term |
Class's shr. of term's tot. occurrences |
#P with term in class |
---|---|---|---|---|---|
1 | Computer Science, Artificial Intelligence | 18788 | 68% | 0% | 187 |
2 | Computer Science, Information Systems | 1689 | 21% | 0% | 57 |
3 | Computer Science, Theory & Methods | 506 | 12% | 0% | 34 |
4 | Engineering, Electrical & Electronic | 357 | 23% | 0% | 63 |
5 | Computer Science, Cybernetics | 138 | 2% | 0% | 6 |
6 | Statistics & Probability | 113 | 5% | 0% | 15 |
7 | Computer Science, Software Engineering | 110 | 5% | 0% | 14 |
8 | Automation & Control Systems | 102 | 4% | 0% | 12 |
9 | Computer Science, Hardware & Architecture | 59 | 3% | 0% | 8 |
10 | Remote Sensing | 40 | 2% | 0% | 5 |
Address terms |
Rank | Term | Chi square | Shr. of publ. in class containing term |
Class's shr. of term's tot. occurrences |
#P with term in class |
---|---|---|---|---|---|
1 | DATA MIN EXPLORAT DMX GRP | 110236 | 0% | 100% | 1 |
2 | SHANGHAI MEDIA PROC COMMUN | 110236 | 0% | 100% | 1 |
3 | SIERRA PROJECT GRP | 110236 | 0% | 100% | 1 |
4 | UNIDAD MANTENIMIENTO MAT MOVIL | 110236 | 0% | 100% | 1 |
5 | ELECT TRICT | 55117 | 0% | 50% | 1 |
6 | GUANGDONG POPULAR HIGH PERFORMANCE COMP | 55117 | 0% | 50% | 1 |
7 | IMAGE UNDERSTANDING SYST FT3AB | 55117 | 0% | 50% | 1 |
8 | SCI TECHNOL DCT | 55117 | 0% | 50% | 1 |
9 | INTELLIGENT EMBEDDED SYST | 44091 | 1% | 20% | 2 |
10 | NOVEL SOFTWARE TECHNOL | 41423 | 4% | 4% | 10 |
Journals |
Rank | Term | Chi square | Shr. of publ. in class containing term |
Class's shr. of term's tot. occurrences |
#P with term in class |
---|---|---|---|---|---|
1 | JOURNAL OF MACHINE LEARNING RESEARCH | 8496 | 4% | 1% | 11 |
2 | LECTURE NOTES IN ARTIFICIAL INTELLIGENCE | 4498 | 9% | 0% | 26 |
3 | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS | 3721 | 2% | 1% | 6 |
4 | PATTERN RECOGNITION LETTERS | 3215 | 5% | 0% | 13 |
5 | PATTERN RECOGNITION | 3141 | 5% | 0% | 14 |
6 | JOURNAL OF INTELLIGENT INFORMATION SYSTEMS | 2942 | 1% | 1% | 4 |
7 | IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE | 2889 | 4% | 0% | 11 |
8 | KNOWLEDGE AND INFORMATION SYSTEMS | 2607 | 2% | 0% | 5 |
9 | INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE | 2521 | 2% | 0% | 6 |
10 | IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING | 2335 | 3% | 0% | 8 |
Author Key Words |
Rank | Term | Chi square | Shr. of publ. in class containing term |
Class's shr. of term's tot. occurrences |
#P with term in class |
LCSH search | Wikipedia search |
---|---|---|---|---|---|---|---|
1 | SEMI SUPERVISED LEARNING | 1071791 | 31% | 11% | 85 | Search SEMI+SUPERVISED+LEARNING | Search SEMI+SUPERVISED+LEARNING |
2 | CO TRAINING | 955341 | 9% | 33% | 26 | Search CO+TRAINING | Search CO+TRAINING |
3 | TRI TRAINING | 306206 | 2% | 56% | 5 | Search TRI+TRAINING | Search TRI+TRAINING |
4 | LABELED AND UNLABELED SAMPLES | 220471 | 1% | 100% | 2 | Search LABELED+AND+UNLABELED+SAMPLES | Search LABELED+AND+UNLABELED+SAMPLES |
5 | LEARNING FROM UNLABELED DATA | 220471 | 1% | 100% | 2 | Search LEARNING+FROM+UNLABELED+DATA | Search LEARNING+FROM+UNLABELED+DATA |
6 | SAFE MECHANISM | 220471 | 1% | 100% | 2 | Search SAFE+MECHANISM | Search SAFE+MECHANISM |
7 | TEMPORAL RBF | 220471 | 1% | 100% | 2 | Search TEMPORAL+RBF | Search TEMPORAL+RBF |
8 | UNLABELED DATA | 207639 | 3% | 21% | 9 | Search UNLABELED+DATA | Search UNLABELED+DATA |
9 | SEMISUPERVISED LEARNING | 175635 | 5% | 11% | 14 | Search SEMISUPERVISED+LEARNING | Search SEMISUPERVISED+LEARNING |
10 | SEMI SUPERVISED CLASSIFICATION | 150990 | 4% | 14% | 10 | Search SEMI+SUPERVISED+CLASSIFICATION | Search SEMI+SUPERVISED+CLASSIFICATION |
Core articles |
The table includes core articles in the class. The following variables is taken into account for the relevance score of an article in a cluster c: (1) Number of references referring to publications in the class. (2) Share of total number of active references referring to publications in the class. (3) Age of the article. New articles get higher score than old articles. (4) Citation rate, normalized to year. |
Rank | Reference | # ref. in cl. |
Shr. of ref. in cl. |
Citations |
---|---|---|---|---|
1 | TRIGUERO, I , GARCIA, S , HERRERA, F , (2015) SEG-SSC: A FRAMEWORK BASED ON SYNTHETIC EXAMPLES GENERATION FOR SELF-LABELED SEMI-SUPERVISED CLASSIFICATION.IEEE TRANSACTIONS ON CYBERNETICS. VOL. 45. ISSUE 4. P. 622 -634 | 20 | 54% | 8 |
2 | TRIGUERO, I , GARCIA, S , HERRERA, F , (2015) SELF-LABELED TECHNIQUES FOR SEMI-SUPERVISED LEARNING: TAXONOMY, SOFTWARE AND EMPIRICAL STUDY.KNOWLEDGE AND INFORMATION SYSTEMS. VOL. 42. ISSUE 2. P. 245 -284 | 23 | 46% | 9 |
3 | FAZAKIS, N , KARLOS, S , KOTSIANTIS, S , SGARBAS, K , (2017) SELF-TRAINED ROTATION FOREST FOR SEMI-SUPERVISED.JOURNAL OF INTELLIGENT & FUZZY SYSTEMS. VOL. 32. ISSUE 1. P. 711 -722 | 16 | 64% | 0 |
4 | FAZAKIS, N , KARLOS, S , KOTSIANTIS, S , SGARBAS, K , (2016) SELF-TRAINED LMT FOR SEMISUPERVISED LEARNING.COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE. VOL. . ISSUE . P. - | 16 | 62% | 0 |
5 | JIANG, Z , ZHAN, YZ , (2015) A NOVEL DIVERSITY-BASED SEMI-SUPERVISED LEARNING FRAMEWORK WITH RELATED THEORETICAL ANALYSIS.INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS. VOL. 24. ISSUE 3. P. - | 9 | 75% | 0 |
6 | ZHOU, ZH , LI, M , (2010) SEMI-SUPERVISED LEARNING BY DISAGREEMENT.KNOWLEDGE AND INFORMATION SYSTEMS. VOL. 24. ISSUE 3. P. 415 -439 | 10 | 48% | 69 |
7 | DONG, AM , CHUNG, FL , WANG, ST , (2016) SEMI-SUPERVISED CLASSIFICATION METHOD THROUGH OVERSAMPLING AND COMMON HIDDEN SPACE.INFORMATION SCIENCES. VOL. 349. ISSUE . P. 216 -228 | 10 | 42% | 1 |
8 | KRITHARA, A , AMINI, MR , GOUTTE, C , RENDERS, JM , (2011) LEARNING ASPECT MODELS WITH PARTIALLY LABELED DATA.PATTERN RECOGNITION LETTERS. VOL. 32. ISSUE 2. P. 297 -304 | 7 | 70% | 1 |
9 | JIANG, Z , ZENG, JP , ZHANG, SY , (2013) INTER-TRAINING: EXPLOITING UNLABELED DATA IN MULTI-CLASSIFIER SYSTEMS.KNOWLEDGE-BASED SYSTEMS. VOL. 45. ISSUE . P. 8-19 | 8 | 53% | 2 |
10 | AZIZYAN, M , SINGH, A , WASSERMAN, L , (2013) DENSITY-SENSITIVE SEMISUPERVISED INFERENCE.ANNALS OF STATISTICS. VOL. 41. ISSUE 2. P. 751-771 | 6 | 67% | 2 |
Classes with closest relation at Level 1 |