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Dean F. Hougen
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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
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
Corrections to "Visual Analytics for Decision-Making During Pandemics". Comput. Sci. Eng. 23(1): 106 (2021) - [c58]Sai Teja Kanneganti, Jin-Song Pei
Developing Interpretable Machine Learning for Forward Kinematics of Robotic Arms. SSCI 2021: 1-9 - 2020
- [j9]Audrey Reinert
Visual Analytics for Decision-Making During Pandemics. Comput. Sci. Eng. 22(6): 48-59 (2020) - [c57]Dean Frederick Hougen
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
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
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
Nurturing promotes the evolution of reinforcement learning in changing environments. SSCI 2017: 1-8 - [c44]Syed Naveed Hussain Shah
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
Robot-to-robot nurturing: A call to the research community. ICDL-EPIROB 2012: 1-2 - 2011
- [j8]Shawn McCarroll, Mark Yeary, Dean F. Hougen
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
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
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
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
A distributed surveillance task using miniature robots. AAMAS 2002: 1393-1394 - 2001
- [c12]Paul E. Rybski, Sascha Stoeter, Maria L. Gini
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
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
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
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
A team of robotic agents for surveillance. Agents 2000: 9-16 - [c8]Sascha Stoeter, Paul E. Rybski, Maria L. Gini, Dean F. Hougen
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
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
Nomadic routing applications for wireless networking in a team of miniature robots. SMC 2000: 3306-3311
1990 – 1999
- 1999
- [j1]Dean F. Hougen
Repeatability of Real World Training Experiments: A Case Study. Auton. Robots 6(3): 281-292 (1999) - 1997
- [c3]Dean F. Hougen
Partitioning input space for reinforcement learning for control. ICNN 1997: 755-760 - [c2]Dean F. Hougen
Rapid unsupervised connectionist learning for backing a robot with two trailers. ICRA 1997: 2950-2955 - 1996
- [c1]Dean F. Hougen
Fast connectionist learning for trailer backing using a real robot. ICRA 1996: 1917-1922
Coauthor Index
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last updated on 2024-08-05 20:12 CEST by the dblp team
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