| 2008 |
|---|
| 25 | | Rohitash Chandra,
Christian W. Omlin:
Hybrid Evolutionary One-Step Gradient Descent for Training Recurrent Neural Networks.
GEM 2008: 305-311 |
| 2007 |
|---|
| 24 | | Rohitash Chandra,
Christian W. Omlin:
A Hybrid Recurrent Neural Networks Architecture Inspired by Hidden Markov Models: Training and Extraction of Deterministic Finite Automaton.
Artificial Intelligence and Pattern Recognition 2007: 278-285 |
| 23 | | Rohitash Chandra,
Christian W. Omlin:
The Comparison and Combination of Genetic and Gradient Descent Learning in Recurrent Neural Networks: An Application to Speech Phoneme Classification.
Artificial Intelligence and Pattern Recognition 2007: 286-293 |
| 22 | | Rohitash Chandra,
Christian W. Omlin:
Knowledge Discovery using Artificial Neural Networks for a Conservation Biology Domain.
DMIN 2007: 221-227 |
| 21 | | Rohitash Chandra,
Christian W. Omlin:
Hybrid Recurrent Neural Networks: An Application to Phoneme Classification.
GEM 2007: 57-62 |
| 2006 |
|---|
| 20 | | Rohitash Chandra,
Christian W. Omlin:
Training and extraction of fuzzy finite state automata in recurrent neural networks.
Computational Intelligence 2006: 274-279 |
| 19 | EE | Jacob Whitehill,
Christian W. Omlin:
Local versus Global Segmentation for Facial Expression Recognition.
FG 2006: 357-362 |
| 18 | EE | Jacob Whitehill,
Christian W. Omlin:
Haar Features for FACS AU Recognition.
FG 2006: 97-101 |
| 17 | EE | Okuthe P. Kogeda,
Johnson I. Agbinya,
Christian W. Omlin:
A Probabilistic Approach To Faults Prediction in Cellular Networks.
ICN/ICONS/MCL 2006: 130 |
| 2005 |
|---|
| 16 | EE | Okuthe P. Kogeda,
Johnson I. Agbinya,
Christian W. Omlin:
Impacts and Cost of Faults on Services in Cellular Networks.
ICMB 2005: 551-555 |
| 2004 |
|---|
| 15 | EE | A. Vahed,
Christian W. Omlin:
A Machine Learning Method for Extracting Symbolic Knowledge from Recurrent Neural Networks.
Neural Computation 16(1): 59-71 (2004) |
| 2003 |
|---|
| 14 | EE | Christian W. Omlin,
Sean Snyders:
Inductive bias strength in knowledge-based neural networks: application to magnetic resonance spectroscopy of breast tissues.
Artificial Intelligence in Medicine 28(2): 121-140 (2003) |
| 2002 |
|---|
| 13 | EE | Dane Walsh,
Christian W. Omlin:
Automatic Detection of Film Orientation with Support Vector Machines.
IEA/AIE 2002: 36-46 |
| 2001 |
|---|
| 12 | EE | Sean Snyders,
Christian W. Omlin:
Inductive Bias in Recurrent Neural Networks.
IWANN (1) 2001: 339-346 |
| 11 | EE | Jacobus van Zyl,
Christian W. Omlin:
Knowledge-Based Neural Networks for Modelling Time Series.
IWANN (2) 2001: 579-586 |
| 2000 |
|---|
| 10 | EE | Andries Kruger,
C. Lee Giles,
Frans Coetzee,
Eric J. Glover,
Gary William Flake,
Steve Lawrence,
Christian W. Omlin:
DEADLINER: Building a New Niche Search Engine.
CIKM 2000: 272-281 |
| 9 | EE | T. Wessels,
Christian W. Omlin:
Refining Hidden Markov Models with Recurrent Neural Networks.
IJCNN (2) 2000: 271-278 |
| 8 | EE | Sean Snyders,
Christian W. Omlin:
What Inductive Bias Gives Good Neural Network Training Performance?
IJCNN (3) 2000: 445-450 |
| 7 | EE | T. Wessels,
Christian W. Omlin:
A Hybrid System for Signature Verification.
IJCNN (5) 2000: 509-514 |
| 1998 |
|---|
| 6 | | Christian W. Omlin,
C. Lee Giles,
Karvel K. Thornber:
Fuzzy Knowledge and Recurrent Neural Networks: A Dynamical Systems Perspective.
Hybrid Neural Systems 1998: 123-143 |
| 1996 |
|---|
| 5 | EE | Christian W. Omlin,
C. Lee Giles:
Rule Revision With Recurrent Neural Networks.
IEEE Trans. Knowl. Data Eng. 8(1): 183-188 (1996) |
| 4 | EE | Christian W. Omlin,
C. Lee Giles:
Constructing Deterministic Finite-State Automata in Recurrent Neural Networks.
J. ACM 43(6): 937-972 (1996) |
| 3 | | Christian W. Omlin,
C. Lee Giles:
Stable encoding of large finite-state automata in recurrent neural networks with sigmoid discriminants.
Neural Computation 8(4): 675-696 (1996) |
| 2 | EE | Christian W. Omlin,
C. Lee Giles:
Extraction of rules from discrete-time recurrent neural networks.
Neural Networks 9(1): 41-52 (1996) |
| 1992 |
|---|
| 1 | | Christian W. Omlin,
C. Lee Giles:
Training Second-Order Recurrent Neural Networks using Hints.
ML 1992: 361-366 |
