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Stefan C. Kremer
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2020 – today
- 2021
- [c29]Matthew Kirchhof
, Christopher J. F. Cameron, Stefan C. Kremer:
End-to-end chromosomal compartment prediction from reference genomes. BIBM 2021: 50-57 - 2020
- [c28]Stefan Schneider, Graham W. Taylor, Stefan C. Kremer
Similarity Learning Networks for Animal Individual Re-Identification - Beyond the Capabilities of a Human Observer. WACV Workshops 2020: 44-52
2010 – 2019
- 2019
- [i5]Stefan Schneider, Graham W. Taylor, Stefan S. Linquist, Stefan C. Kremer:
Similarity Learning Networks for Animal Individual Re-Identification - Beyond the Capabilities of a Human Observer. CoRR abs/1902.09324 (2019) - 2018
- [j17]Tarfa Hamed
Network intrusion detection system based on recursive feature addition and bigram technique. Comput. Secur. 73: 137-155 (2018) - [j16]Eddie Y. T. Ma
Machine Learned Replacement of N-Labels for Basecalled Sequences in DNA Barcoding. IEEE ACM Trans. Comput. Biol. Bioinform. 15(1): 191-204 (2018) - [c27]Stefan Schneider, Graham W. Taylor, Stefan C. Kremer:
Deep Learning Object Detection Methods for Ecological Camera Trap Data. CRV 2018: 321-328 - [p6]Tarfa Hamed
A Survey and Taxonomy of Classifiers of Intrusion Detection Systems. Computer and Network Security Essentials 2018: 21-39 - [p5]Tarfa Hamed
A Survey and Taxonomy on Data and Pre-processing Techniques of Intrusion Detection Systems. Computer and Network Security Essentials 2018: 113-134 - [p4]Jason B. Ernst, Tarfa Hamed
A Survey and Comparison of Performance Evaluation in Intrusion Detection Systems. Computer and Network Security Essentials 2018: 555-568 - [i4]Stefan Schneider, Graham W. Taylor, Stefan C. Kremer:
Deep Learning Object Detection Methods for Ecological Camera Trap Data. CoRR abs/1803.10842 (2018) - [i3]Stefan Schneider, Graham W. Taylor, Stefan S. Linquist, Stefan C. Kremer:
Past, Present, and Future Approaches Using Computer Vision for Animal Re-Identification from Camera Trap Data. CoRR abs/1811.07749 (2018) - 2016
- [j15]Sajid A. Marhon
Prediction of Protein Coding Regions Using a Wide-Range Wavelet Window Method. IEEE ACM Trans. Comput. Biol. Bioinform. 13(4): 742-753 (2016) - [c26]Masood Zamani, Stefan C. Kremer:
Protein secondary structure prediction through a novel framework of secondary structure transition sites and new encoding schemes. CIBCB 2016: 1-7 - 2015
- [j14]Sajid A. Marhon
A dynamic representation-based, de novo method for protein-coding region prediction and biological information detection. Digit. Signal Process. 46: 10-18 (2015) - [j13]William B. Gardner, Gary Gréwal, Deborah Stacey
A new Canadian interdisciplinary Ph.D. in computational sciences. J. Comput. Sci. 9: 82-87 (2015) - [c25]Masood Zamani, Stefan C. Kremer:
A multi-stage protein secondary structure prediction system using machine learning and information theory. BIBM 2015: 1304-1309 - [c24]Masood Zamani, Stefan C. Kremer:
Protein secondary structure prediction using an evolutionary computation method and clustering. CIBCB 2015: 1-6 - [c23]Jason B. Ernst, Stefan C. Kremer, Joel J. P. C. Rodrigues:
Heterogeneous wireless network RAT selection with multiple operators and service contracts. ICC 2015: 6011-6017 - [c22]Jason B. Ernst, Stefan C. Kremer, Joel J. P. C. Rodrigues:
Performance evaluation of heterogeneous wireless networks considering competing objectives and viewpoints. SAC 2015: 680-687 - 2014
- [j12]Jason B. Ernst
A survey of QoS/QoE mechanisms in heterogeneous wireless networks. Phys. Commun. 13: 61-72 (2014) - [c21]Jason B. Ernst, Stefan C. Kremer, Joel J. P. C. Rodrigues:
A Wi-Fi Simulation Model Which Supports Channel Scanning across Multiple Non-overlapping Channels in NS3. AINA 2014: 268-275 - [c20]Sajid A. Marhon
A New, Dynamic-Representation-Based Gene Finding Method with an Analysis of False Positive Peaks. BIBE 2014: 198-203 - [c19]Jason B. Ernst, Stefan C. Kremer, Joel J. P. C. Rodrigues:
A utility based access point selection method for IEEE 802.11 wireless networks with enhanced quality of experience. ICC 2014: 2363-2368 - [c18]Tarfa Hamed
An Accurate, Fast Embedded Feature Selection for SVMs. ICMLA 2014: 135-140 - 2013
- [p3]Sajid Marhon
Recurrent Neural Networks. Handbook on Neural Information Processing 2013: 29-65 - [p2]Masood Zamani, Stefan C. Kremer:
Neural Networks in Bioinformatics. Handbook on Neural Information Processing 2013: 505-525 - 2012
- [c17]Sajid Marhon
A brief comparison of DSP and HMM methods for gene finding. BCB 2012: 582-584 - [c16]Masood Zamani, Stefan C. Kremer:
Protein secondary structure prediction using support vector machines and a codon encoding scheme. BIBM Workshops 2012: 22-27 - [c15](Zhou) Bryan Bai, Stefan C. Kremer:
Sequence learning: analysis and solutions for sparse data in high dimensional spaces. CIBCB 2012: 298-305 - 2011
- [j11]Sajid Marhon
Gene Prediction Based on DNA Spectral Analysis: A Literature Review. J. Comput. Biol. 18(4): 639-676 (2011) - [c14](Zhou) Bryan Bai, Stefan C. Kremer:
Regularization of sequence data for machine learning. BIBM Workshops 2011: 19-25 - [c13]Masood Zamani, Stefan C. Kremer:
Amino acid encoding schemes for machine learning methods. BIBM Workshops 2011: 327-333 - [c12]Sajid Marhon
Protein coding region prediction based on the adaptive representation method. CCECE 2011: 415-418 - 2010
- [j10]Sajid Marhon
Theoretical justification of computing the 3-base periodicity using nucleotide distribution variance. Biosyst. 101(3): 185-186 (2010) - [j9]Eddie Y. T. Ma, Christopher J. F. Cameron
Classifying and scoring of molecules with the NGN: new datasets, significance tests, and generalization. BMC Bioinform. 11(S-8): S4 (2010)
2000 – 2009
- 2009
- [c11]Eddie Y. T. Ma, Stefan C. Kremer:
Neural Grammar Networks in QSAR Chemistry. BIBM 2009: 37-42 - [p1]Eddie Y. T. Ma, Stefan C. Kremer:
Neural Grammar Networks. Innovations in Neural Information Paradigms and Applications 2009: 67-96 - [e1]Michael Biehl, Barbara Hammer, Sepp Hochreiter, Stefan C. Kremer, Thomas Villmann:
Similarity-based learning on structures, 15.02. - 20.02.2009. Dagstuhl Seminar Proceedings 09081, Schloss Dagstuhl - Leibniz-Zentrum für Informatik, Germany 2009 [contents] - [i2]Michael Biehl, Barbara Hammer, Sepp Hochreiter, Stefan C. Kremer, Thomas Villmann:
09081 Abstracts Collection - Similarity-based learning on structures. Similarity-based learning on structures 2009 - [i1]Michael Biehl, Barbara Hammer, Sepp Hochreiter, Stefan C. Kremer, Thomas Villmann:
09081 Summary - Similarity-based learning on structures. Similarity-based learning on structures 2009 - 2007
- [r1]Stefan C. Kremer:
Spatio-Temporal Connectionist Networks. Handbook of Dynamic System Modeling 2007 - 2006
- [c10]Mosaab Daoud
Detecting Similarities between Families of Bio-sequences using the Steady-State of a PCA-Neural Network. CIBCB 2006: 1-7 - [c9]Patrick Carter, Stefan C. Kremer:
Fuzzy Grammar Induction from Large Corpora. FUZZ-IEEE 2006: 2083-2089 - [c8]Mansoor Doostfatemeh
General Fuzzy Automata, New Efficient Acceptors for Fuzzy Languages. FUZZ-IEEE 2006: 2097-2103 - [c7]Mosaab Daoud, Stefan C. Kremer:
Neural and Statistical Classification to Families of Bio-sequences. IJCNN 2006: 699-704 - 2005
- [j8]Mansoor Doostfatemeh
New directions in fuzzy automata. Int. J. Approx. Reason. 38(2): 175-214 (2005) - [c6]Mansoor Doostfatemeh, Stefan C. Kremer:
Biological Sequence Prediction using General Fuzzy Automata. CIBCB 2005: 406-413 - 2003
- [j7]Orlando Cicchello, Stefan C. Kremer:
Inducing Grammars from Sparse Data Sets: A Survey of Algorithms and Results. J. Mach. Learn. Res. 4: 603-632 (2003) - [j6]Guilherme De A. Barreto
A Taxonomy for Spatiotemporal Connectionist Networks Revisited: The Unsupervised Case. Neural Comput. 15(6): 1255-1320 (2003) - 2002
- [c5]Orlando Cicchello, Stefan C. Kremer:
Beyond EDSM. ICGI 2002: 37-48 - 2001
- [j5]André Carlos Ponce de Leon Ferreira de Carvalho
Introduction By Guest Editors: Non-Gradient Learning Techniques. Int. J. Neural Syst. 11(6): vii-ix (2001) - [j4]Stefan C. Kremer
Spatiotemporal Connectionist Networks: A Taxonomy and Review. Neural Comput. 13(2): 249-306 (2001)
1990 – 1999
- 1999
- [j3]Stefan C. Kremer:
Identification of a specific limitation on local-feedback recurrent networks acting as Mealy-Moore machines. IEEE Trans. Neural Networks 10(2): 433-438 (1999) - 1998
- [c4]Stefan C. Kremer, John F. Kolen:
Dynamical Recurrent Networks for Sequential Data Processing. Hybrid Neural Systems 1998: 107-122 - 1997
- [c3]Stefan C. Kremer:
Natural properties in an artificial neural network. ICNN 1997: 612-616 - [c2]Stefan C. Kremer:
Parallel stochastic grammar induction. ICNN 1997: 1424-1428 - 1996
- [j2]Stefan C. Kremer:
Comments on "Constructive learning of recurrent neural networks: limitations of recurrent cascade correlation and a simple solution". IEEE Trans. Neural Networks 7(4): 1047-1051 (1996) - 1995
- [j1]Stefan C. Kremer:
On the computational power of Elman-style recurrent networks. IEEE Trans. Neural Networks 6(4): 1000-1004 (1995) - [c1]Stefan C. Kremer:
Finite State Automata that Recurrent Cascade-Correlation Cannot Represent. NIPS 1995: 612-618
Coauthor Index
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