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2020 – today
- 2023
[c60]Sanjana Mudduluru, Sai Kiran Reddy Maryada, William Booker, Dean F. Hougen
, Bin Zheng:
Improving medical image segmentation and classification using a novel joint deep learning model. Medical Imaging: Computer-Aided Diagnosis 2023- 2022
- [c59]Sai Kiran Reddy Maryada, William Booker, Gopichandh Danala, Catherine An Ha, Sanjana Mudduluru, Dean F. Hougen, Bin Zheng:
Applying a novel two-stage deep-learning model to improve accuracy in detecting retinal fundus images. Medical Imaging: Computer-Aided Diagnosis 2022 - 2021
- [j10]Audrey Reinert, Luke S. Snyder, Jieqiong Zhao, Andrew S. Fox, Dean F. Hougen, Charles D. Nicholson, David S. Ebert:
Corrections to "Visual Analytics for Decision-Making During Pandemics". Comput. Sci. Eng. 23(1): 106 (2021) - [c58]Sai Teja Kanneganti, Jin-Song Pei, Dean Frederick Hougen:
Developing Interpretable Machine Learning for Forward Kinematics of Robotic Arms. SSCI 2021: 1-9 - 2020
- [j9]Audrey Reinert, Luke S. Snyder, Jieqiong Zhao, Andrew S. Fox, Dean F. Hougen, Charles D. Nicholson, David S. Ebert:
Visual Analytics for Decision-Making During Pandemics. Comput. Sci. Eng. 22(6): 48-59 (2020) - [c57]Dean Frederick Hougen, Jin-Song Pei, Sai Teja Kanneganti:
Toward Interpretable Machine Learning for Understanding Epidemic Data. IEEE BigData 2020: 3677-3681 - [c56]Syed Naveed Hussain Shah, Dean Frederick Hougen:
Stochastic Reinforcement Learning for Continuous Actions in Dynamic Environments. FLAIRS 2020: 472-477 - [c55]Britt Richardson, Dean F. Hougen:
Districts by Demographics: Predicting U.S. House of Representative Elections using Machine Learning and Demographic Data. ICMLA 2020: 833-838 - [c54]Syed Naveed Hussain Shah, Dean Frederick Hougen:
Improved Stochastic Synapse Reinforcement Learning for Continuous Actions in Sharply Changing Environments. IJCNN 2020: 1-8 - [c53]Oluwatobi I. Ajagbe, Dean Frederick Hougen:
Evolution, Sympatric Speciation, and Risk Aversion. SSCI 2020: 1171-1178
2010 – 2019
- 2019
- [c52]Syed Naveed Hussain Shah, Dean Frederick Hougen:
Rethinking Stochasticity in Neural Networks for Reinforcement Learning with Continuous Actions*. SSCI 2019: 488-496 - [c51]Dean Frederick Hougen, Syed Naveed Hussain Shah:
The Evolution of Reinforcement Learning *. SSCI 2019: 1457-1464 - [c50]Steven Aaron Roberts, Dean Frederick Hougen:
Information and Resource Sharing in Reinforcement Learning Agents Subject to Risk. SSCI 2019: 1959-1966 - 2018
- [c49]William Booker, Dean Frederick Hougen:
Meiotic Inheritance and Gene Dominance in Synthetic Sympatric Speciation. CEC 2018: 1-8 - [c48]Bryan Hoke, Dean Frederick Hougen:
Nurturing Promotes the Evolution of Generalized Supervised Learning. CEC 2018: 1-8 - [c47]Joohee Suh, Dean Frederick Hougen:
The Context-Aware Learning Model: experience-powered Logistic Regression Backpropagation (CALM-epLRB). IJCNN 2018: 1-8 - [c46]Joohee Suh, Dean Frederick Hougen:
The Context-Aware Learning Model: neuro-experience-powered Logistic Regression Backpropagation (CALM-nepLRB). IJCNN 2018: 1-8 - 2017
- [c45]Syed Naveed Hussain Shah, Dean F. Hougen:
Nurturing promotes the evolution of reinforcement learning in changing environments. SSCI 2017: 1-8 - [c44]Syed Naveed Hussain Shah, Dean F. Hougen:
Stochastic synapse reinforcement learning (SSRL). SSCI 2017: 1-8 - [c43]Joohee Suh, Dean F. Hougen:
The context-aware learning model: Reward-based and experience-based logistic regression backpropagation. SSCI 2017: 1-8 - 2014
- [c42]Joohee Suh, Dean F. Hougen:
Context-based adaptive robot behavior learning model (CARB-LM). CICA 2014: 206-213 - 2012
- [c41]Mark Woehrer, Dean F. Hougen, Ingo Schlupp:
Sexual Selection, Resource Distribution, and Population Size in Synthetic Sympatric Speciation. ALIFE 2012 - [c40]Brent E. Eskridge, Dean F. Hougen:
Nurturing promotes the evolution of learning in uncertain environments. ICDL-EPIROB 2012: 1-6 - [c39]Armand Leonce, Bryan Hoke, Dean F. Hougen:
Evolution of robot-to-robot nurturing and nurturability. ICDL-EPIROB 2012: 1-7 - [c38]Mark Woehrer, Dean F. Hougen, Ingo Schlupp, Brent E. Eskridge:
Robot-to-robot nurturing: A call to the research community. ICDL-EPIROB 2012: 1-2 - 2011
- [j8]Shawn McCarroll, Mark Yeary, Dean F. Hougen, Valliappa Lakshmanan, Steven Smith:
Approaches for Compression of Super-Resolution WSR-88D Data. IEEE Geosci. Remote. Sens. Lett. 8(2): 191-195 (2011) - 2010
- [j7]Brent E. Eskridge, Dean F. Hougen:
Extending adaptive fuzzy behavior hierarchies to multiple levels of composite behaviors. Robotics Auton. Syst. 58(9): 1076-1084 (2010) - [c37]Benjamin P. Carlson, Dean F. Hougen:
Phenotype feedback genetic algorithm operators for heuristic encoding of snakes within hypercubes. GECCO 2010: 791-798 - [c36]John Crofford, Brent E. Eskridge, Dean F. Hougen:
Applying the triple parameter hypothesis to maintenance scheduling. GECCO 2010: 799-806
2000 – 2009
- 2009
- [c35]Pedro A. Diaz-Gomez, Dean F. Hougen:
Three interconnected parameters for genetic algorithms. GECCO 2009: 763-770 - [c34]Brent E. Eskridge, Dean F. Hougen:
Using action abstraction to evolve effective controllers. GECCO 2009: 1773-1774 - [c33]Gerardo Gonzalez, Dean F. Hougen:
Elitism, fitness, and growth. GECCO 2009: 1851-1852 - 2008
- [j6]Vykinta Kligyte, Richard T. Marcy, Ethan P. Waples, Sydney T. Sevier, Elaine S. Godfrey, Michael D. Mumford, Dean F. Hougen:
Application of a Sensemaking Approach to Ethics Training in the Physical Sciences and Engineering. Sci. Eng. Ethics 14(2): 251-278 (2008) - [c32]Pedro A. Diaz-Gomez, Dean F. Hougen:
Internal vs. External Parameters in Fitness Functions. Artificial Intelligence and Pattern Recognition 2008: 195-199 - [c31]Nathaniel P. Troutman, Brent E. Eskridge, Dean F. Hougen:
Is "best-so-far" a good algorithmic performance metric? GECCO 2008: 1147-1148 - [c30]Pedro A. Diaz-Gomez, Dean F. Hougen:
Initial Population Diversity Does Not Influence Performance. GEM 2008: 55-63 - 2007
- [c29]Pedro A. Diaz-Gomez, Dean F. Hougen:
A Trade-Off of the Schema Theorem. Artificial Intelligence and Pattern Recognition 2007: 326-333 - [c28]Pedro A. Diaz-Gomez, Dean F. Hougen:
Empirical Study: Initial Population Diversity and Genetic Algorithm Performance. Artificial Intelligence and Pattern Recognition 2007: 334-341 - [c27]Brent E. Eskridge, Dean F. Hougen:
Using priorities to simplify behavior coordination. AAMAS 2007: 236 - [c26]Pedro A. Diaz-Gomez, Dean F. Hougen:
Initial Population for Genetic Algorithms: A Metric Approach. GEM 2007: 43-49 - [c25]Pedro A. Diaz-Gomez, Dean F. Hougen:
Misuse Detection - An Iterative Process vs. A Genetic Algorithm Approach. ICEIS (2) 2007: 455-458 - [c24]Pedro A. Diaz-Gomez, Dean F. Hougen:
Misuse Detection - A Neural Network vs. A Genetic Algorithm Approach. ICEIS (2) 2007: 459-462 - 2006
- [c23]Jason M. Black, Dean F. Hougen:
A Benchmark for Cooperative Learning Agents. AAAI 2006: 855-1856 - [c22]Brent E. Eskridge, Dean F. Hougen:
An Analysis of Memetic Crossover's Impact on a Population. IEEE Congress on Evolutionary Computation 2006: 1953-1959 - [c21]Brent E. Eskridge, Dean F. Hougen:
Prioritizing Fuzzy Behaviors in Multi-robot Pursuit Teams. FUZZ-IEEE 2006: 1119-1125 - [c20]Pedro A. Diaz-Gomez, Dean F. Hougen:
The snake in the box problem: mathematical conjecture and a genetic algorithm approach. GECCO 2006: 1409-1410 - [c19]Pedro A. Diaz-Gomez, Dean F. Hougen:
Genetic Algorithms for Hunting Snakes in Hypercubes: Fitness Function Analysis and Open Questions. SNPD 2006: 389-394 - 2005
- [c18]Pedro A. Diaz-Gomez, Dean F. Hougen:
Analysis of an Off-Line Intrusion Detection System: A Case Study in Multi-Objective Genetic Algorithms. FLAIRS 2005: 822-823 - [c17]Pedro A. Diaz-Gomez, Dean F. Hougen:
Analysis and mathematical justification of a fitness function used in an intrusion detection system. GECCO 2005: 1591-1592 - [c16]Pedro A. Diaz-Gomez, Dean F. Hougen:
Improved Off-Line Intrusion Detection Using a Genetic Algorithm. ICEIS (2) 2005: 66-73 - 2004
- [c15]Brent E. Eskridge, Dean F. Hougen:
Imitating success: a memetic crossover operator for genetic programming. IEEE Congress on Evolutionary Computation 2004: 809-815 - [c14]Brent E. Eskridge, Dean F. Hougen:
Memetic Crossover for Genetic Programming: Evolution Through Imitation. GECCO (2) 2004: 459-470 - 2002
- [j5]Paul E. Rybski, Sascha Stoeter, Nikolaos P. Papanikolopoulos, Ian T. Burt, Tom Dahlin, Maria L. Gini, Dean F. Hougen, Donald G. Krantz, Florent Nageotte:
Sharing control [multiple miniature robots]. IEEE Robotics Autom. Mag. 9(4): 41-48 (2002) - [j4]Sascha Stoeter, Paul E. Rybski, Kristen Stubbs, Colin McMillen, Maria L. Gini, Dean F. Hougen, Nikolaos Papanikolopoulos:
A robot team for surveillance tasks: Design and architecture. Robotics Auton. Syst. 40(2-3): 173-183 (2002) - [j3]Paul E. Rybski, Sascha Stoeter, Maria L. Gini, Dean F. Hougen, Nikolaos Papanikolopoulos:
Performance of a distributed robotic system using shared communications channels. IEEE Trans. Robotics Autom. 18(5): 713-727 (2002) - [c13]Paul E. Rybski, Maria L. Gini, Dean F. Hougen, Sascha Stoeter, Nikolaos Papanikolopoulos:
A distributed surveillance task using miniature robots. AAMAS 2002: 1393-1394 - 2001
- [c12]Paul E. Rybski, Sascha Stoeter, Maria L. Gini, Dean F. Hougen, Nikolaos Papanikolopoulos:
Dynamic scheduling of a fixed bandwidth communications channel for controlling multiple robots. Agents 2001: 153-154 - [c11]Paul E. Rybski, Sascha Stoeter, Maria L. Gini, Dean F. Hougen, Nikolaos Papanikolopoulos:
Performance of a Distributed Robotic System Using Shared Communication Channels. Advances in Plan-Based Control of Robotic Agents 2001: 211-225 - [c10]Paul E. Rybski, Sascha Stoeter, Maria L. Gini, Dean F. Hougen, Nikolaos Papanikolopoulos:
Effects of limited bandwidth communications channels on the control of multiple robots. IROS 2001: 369-374 - 2000
- [j2]Paul E. Rybski, Nikolaos P. Papanikolopoulos, Sascha Stoeter, Donald G. Krantz, Kemal Berk Yesin, Maria L. Gini, Richard M. Voyles, Dean F. Hougen, Bradley J. Nelson, Michael D. Erickson:
Enlisting rangers and scouts for reconnaissance and surveillance. IEEE Robotics Autom. Mag. 7(4): 14-24 (2000) - [c9]Paul E. Rybski, Sascha Stoeter, Michael D. Erickson, Maria L. Gini, Dean F. Hougen, Nikolaos Papanikolopoulos:
A team of robotic agents for surveillance. Agents 2000: 9-16 - [c8]Sascha Stoeter, Paul E. Rybski, Maria L. Gini, Dean F. Hougen, Nikolaos Papanikolopoulos:
Verteilte Steuerung heterogener Mobiler Roboter. AMS 2000: 270-277 - [c7]Dean F. Hougen, Michael D. Erickson, Paul E. Rybski, Sascha Stoeter, Maria L. Gini, Nikolaos Papanikolopoulos:
Autonomous Mobile Robots and Distributed Exploratory Missions. DARS 2000: 221-230 - [c6]Dean F. Hougen, Maria L. Gini, James R. Slagle:
An Integrated Connectionist Approach to Reinforcement Learning for Robotic Control. ICML 2000: 383-390 - [c5]Dean F. Hougen, Saifallah Benjaafar, Jordan Bonney, John Budenske, Mark Dvorak, Maria L. Gini, Howard French, Donald G. Krantz, Perry Y. Li, Fred Malver, Bradley J. Nelson, Nikolaos Papanikolopoulos, Paul E. Rybski, Sascha Stoeter, Richard M. Voyles, Kemal Berk Yesin:
A Miniature Robotic System for Reconnaissance and Surveillance. ICRA 2000: 501-507 - [c4]John R. Budenske, Jordan Bonney, Atiq Ahamad, Ranga S. Ramanujan, Dean F. Hougen, Nikos Papanikolopoulos:
Nomadic routing applications for wireless networking in a team of miniature robots. SMC 2000: 3306-3311
1990 – 1999
- 1999
- [j1]Dean F. Hougen, Paul E. Rybski, Maria L. Gini:
Repeatability of Real World Training Experiments: A Case Study. Auton. Robots 6(3): 281-292 (1999) - 1997
- [c3]Dean F. Hougen, Maria L. Gini, James R. Slagle:
Partitioning input space for reinforcement learning for control. ICNN 1997: 755-760 - [c2]Dean F. Hougen, Maria L. Gini, James R. Slagle:
Rapid unsupervised connectionist learning for backing a robot with two trailers. ICRA 1997: 2950-2955 - 1996
- [c1]Dean F. Hougen, John Fischer, Maria L. Gini, James R. Slagle:
Fast connectionist learning for trailer backing using a real robot. ICRA 1996: 1917-1922
Coauthor Index
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