Class information for: |
Basic class information |
Class id | #P | Avg. number of references |
Database coverage of references |
---|---|---|---|
6790 | 1443 | 23.6 | 38% |
Hierarchy of classes |
The table includes all classes above and classes immediately below the current class. |
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 | FEEDFORWARD NEURAL NETWORKS | authKW | 400252 | 6% | 22% | 87 |
2 | BACKPROPAGATION | authKW | 219185 | 7% | 10% | 108 |
3 | ONLINE GRADIENT METHOD | authKW | 171389 | 1% | 90% | 9 |
4 | IEEE TRANSACTIONS ON NEURAL NETWORKS | journal | 114478 | 9% | 4% | 125 |
5 | ADAPTIVE MOMENTUM | authKW | 105797 | 0% | 100% | 5 |
6 | NEURAL NETWORKS | journal | 101648 | 9% | 4% | 123 |
7 | CONSTRAINED LEARNING ALGORITHM | authKW | 84637 | 0% | 100% | 4 |
8 | MAPPING SENSITIVITY | authKW | 84637 | 0% | 100% | 4 |
9 | SMOOTHING L 1 2 REGULARIZATION | authKW | 84637 | 0% | 100% | 4 |
10 | WEIGHT INITIALIZATION | authKW | 76169 | 0% | 60% | 6 |
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 | 62439 | 54% | 0% | 780 |
2 | Computer Science, Theory & Methods | 4007 | 15% | 0% | 216 |
3 | Engineering, Electrical & Electronic | 1779 | 22% | 0% | 322 |
4 | Computer Science, Cybernetics | 753 | 2% | 0% | 32 |
5 | Computer Science, Information Systems | 744 | 6% | 0% | 93 |
6 | Neurosciences | 610 | 14% | 0% | 205 |
7 | Computer Science, Interdisciplinary Applications | 526 | 6% | 0% | 83 |
8 | Operations Research & Management Science | 330 | 4% | 0% | 56 |
9 | Automation & Control Systems | 278 | 3% | 0% | 47 |
10 | Computer Science, Hardware & Architecture | 236 | 3% | 0% | 37 |
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 | PLATFORM SEARCH GRP | 42319 | 0% | 100% | 2 |
2 | UP ARTIFICIAL INTELLIGENCE | 42319 | 0% | 100% | 2 |
3 | HEFEI INTELLIGENT MACHINES | 32927 | 1% | 9% | 18 |
4 | INFORMAT POLICY STUDIES | 30772 | 0% | 36% | 4 |
5 | HUMAN ARTIFICIAL INTELLIGENCE SYST | 29034 | 1% | 15% | 9 |
6 | NONLINEAR UNCERTAIN SYST GRP | 28211 | 0% | 67% | 2 |
7 | UPAIRC | 25382 | 0% | 20% | 6 |
8 | INTELLIGENT COMP | 24809 | 1% | 9% | 13 |
9 | UNIT INTELLIGENT CONTROL DESIGN OPTIMIZAT C | 23801 | 0% | 38% | 3 |
10 | ALMECH | 21159 | 0% | 100% | 1 |
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 | IEEE TRANSACTIONS ON NEURAL NETWORKS | 114478 | 9% | 4% | 125 |
2 | NEURAL NETWORKS | 101648 | 9% | 4% | 123 |
3 | NEURAL PROCESSING LETTERS | 49910 | 3% | 5% | 48 |
4 | NEUROCOMPUTING | 42625 | 10% | 1% | 139 |
5 | NEURAL COMPUTATION | 14788 | 3% | 2% | 42 |
6 | NEURAL COMPUTING & APPLICATIONS | 10400 | 2% | 1% | 34 |
7 | NEURAL NETWORK WORLD | 8816 | 1% | 3% | 14 |
8 | LECTURE NOTES IN COMPUTER SCIENCE | 5843 | 11% | 0% | 159 |
9 | LECTURE NOTES IN ARTIFICIAL INTELLIGENCE | 3390 | 4% | 0% | 52 |
10 | SYSTEMS AND COMPUTERS IN JAPAN | 3267 | 0% | 2% | 7 |
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 | FEEDFORWARD NEURAL NETWORKS | 400252 | 6% | 22% | 87 | Search FEEDFORWARD+NEURAL+NETWORKS | Search FEEDFORWARD+NEURAL+NETWORKS |
2 | BACKPROPAGATION | 219185 | 7% | 10% | 108 | Search BACKPROPAGATION | Search BACKPROPAGATION |
3 | ONLINE GRADIENT METHOD | 171389 | 1% | 90% | 9 | Search ONLINE+GRADIENT+METHOD | Search ONLINE+GRADIENT+METHOD |
4 | ADAPTIVE MOMENTUM | 105797 | 0% | 100% | 5 | Search ADAPTIVE+MOMENTUM | Search ADAPTIVE+MOMENTUM |
5 | CONSTRAINED LEARNING ALGORITHM | 84637 | 0% | 100% | 4 | Search CONSTRAINED+LEARNING+ALGORITHM | Search CONSTRAINED+LEARNING+ALGORITHM |
6 | MAPPING SENSITIVITY | 84637 | 0% | 100% | 4 | Search MAPPING+SENSITIVITY | Search MAPPING+SENSITIVITY |
7 | SMOOTHING L 1 2 REGULARIZATION | 84637 | 0% | 100% | 4 | Search SMOOTHING+L+1+2+REGULARIZATION | Search SMOOTHING+L+1+2+REGULARIZATION |
8 | WEIGHT INITIALIZATION | 76169 | 0% | 60% | 6 | Search WEIGHT+INITIALIZATION | Search WEIGHT+INITIALIZATION |
9 | MOMENTUM TERM | 75566 | 0% | 71% | 5 | Search MOMENTUM+TERM | Search MOMENTUM+TERM |
10 | NEURAL NETWORK TRAINING | 73136 | 1% | 31% | 11 | Search NEURAL+NETWORK+TRAINING | Search NEURAL+NETWORK+TRAINING |
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 | CARTWRIGHT, H , CURTEANU, S , (2011) NEURAL NETWORKS APPLIED IN CHEMISTRY. I. DETERMINATION OF THE OPTIMAL TOPOLOGY OF MULTILAYER PERCEPTRON NEURAL NETWORKS.JOURNAL OF CHEMOMETRICS. VOL. 25. ISSUE 10. P. 527 -549 | 52 | 39% | 24 |
2 | THOMAS, P , SUHNER, MC , (2015) A NEW MULTILAYER PERCEPTRON PRUNING ALGORITHM FOR CLASSIFICATION AND REGRESSION APPLICATIONS.NEURAL PROCESSING LETTERS. VOL. 42. ISSUE 2. P. 437 -458 | 28 | 64% | 0 |
3 | ZHANG, HS , TANG, YL , (2017) ONLINE GRADIENT METHOD WITH SMOOTHING L(0) REGULARIZATION FOR FEEDFORWARD NEURAL NETWORKS.NEUROCOMPUTING. VOL. 224. ISSUE . P. 1 -8 | 23 | 70% | 0 |
4 | ISLAM, MM , SATTAR, MA , AMIN, MF , YAO, X , MURASE, K , (2009) A NEW ADAPTIVE MERGING AND GROWING ALGORITHM FOR DESIGNING ARTIFICIAL NEURAL NETWORKS.IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS. VOL. 39. ISSUE 3. P. 705 -722 | 25 | 69% | 34 |
5 | WANG, J , YANG, J , WU, W , (2011) CONVERGENCE OF CYCLIC AND ALMOST-CYCLIC LEARNING WITH MOMENTUM FOR FEEDFORWARD NEURAL NETWORKS.IEEE TRANSACTIONS ON NEURAL NETWORKS. VOL. 22. ISSUE 8. P. 1297 -1306 | 20 | 83% | 8 |
6 | KATHIRVALAVAKUMAR, T , SUBAVATHI, SJ , (2009) NEIGHBORHOOD BASED MODIFIED BACKPROPAGATION ALGORITHM USING ADAPTIVE LEARNING PARAMETERS FOR TRAINING FEEDFORWARD NEURAL NETWORKS.NEUROCOMPUTING. VOL. 72. ISSUE 16-18. P. 3915 -3921 | 17 | 100% | 11 |
7 | FAN, QW , WU, W , ZURADA, JM , (2016) CONVERGENCE OF BATCH GRADIENT LEARNING WITH SMOOTHING REGULARIZATION AND ADAPTIVE MOMENTUM FOR NEURAL NETWORKS.SPRINGERPLUS. VOL. 5. ISSUE . P. - | 17 | 71% | 3 |
8 | MAGOULAS, GD , VRAHATIS, MN , (2006) ADAPTIVE ALGORITHMS FOR NEURAL NETWORK SUPERVISED LEARNING: A DETERMINISTIC OPTIMIZATION APPROACH.INTERNATIONAL JOURNAL OF BIFURCATION AND CHAOS. VOL. 16. ISSUE 7. P. 1929 -1950 | 26 | 70% | 1 |
9 | WU, W , WANG, JA , CHENG, MS , LI, ZX , (2011) CONVERGENCE ANALYSIS OF ONLINE GRADIENT METHOD FOR BP NEURAL NETWORKS.NEURAL NETWORKS. VOL. 24. ISSUE 1. P. 91-98 | 14 | 100% | 27 |
10 | RADI, A , POLI, R , (1999) GENETIC PROGRAMMING DISCOVERS EFFICIENT LEARNING RULES FOR THE HIDDEN AND OUTPUT LAYERS OF FEEDFORWARD NEURAL NETWORKS.GENETIC PROGRAMMING. VOL. 1598. ISSUE . P. 120 -134 | 23 | 92% | 0 |
Classes with closest relation at Level 1 |