2000 - IEEE Fellow For development of the gamma neural model and for its applications in signal processing.
Jose C. Principe spends much of his time researching Artificial intelligence, Algorithm, Pattern recognition, Artificial neural network and Entropy. Jose C. Principe has included themes like Machine learning and Signal processing in his Artificial intelligence study. His Algorithm study incorporates themes from Mean squared error, Kernel, Kernel method and Mathematical optimization.
His Pattern recognition study combines topics in areas such as Time delay neural network, Measure, Outlier and Blind signal separation. His Artificial neural network study integrates concerns from other disciplines, such as Control theory and Nonlinear system. His Entropy study combines topics from a wide range of disciplines, such as Information theory, Probability density function and Estimator.
The scientist’s investigation covers issues in Artificial intelligence, Algorithm, Pattern recognition, Artificial neural network and Machine learning. The study incorporates disciplines such as Electroencephalography, Computer vision and Signal processing in addition to Artificial intelligence. The concepts of his Algorithm study are interwoven with issues in Entropy, Mathematical optimization, Kernel and Nonlinear system.
His studies deal with areas such as Information theory and Estimator as well as Entropy. As part of his studies on Pattern recognition, he often connects relevant subjects like Blind signal separation. His research related to Time delay neural network and Backpropagation might be considered part of Artificial neural network.
Jose C. Principe mainly focuses on Artificial intelligence, Algorithm, Pattern recognition, Kernel and Machine learning. His Artificial intelligence study often links to related topics such as Computer vision. His Algorithm research is multidisciplinary, incorporating perspectives in Matrix, Nonlinear system, Entropy, Similarity measure and Robustness.
His Robustness research incorporates elements of Mean squared error, Adaptive filter and Outlier. In his study, Pixel is strongly linked to Image, which falls under the umbrella field of Pattern recognition. His Kernel study deals with Kernel intersecting with Training set and Feature vector.
His scientific interests lie mostly in Artificial intelligence, Algorithm, Information theory, Pattern recognition and Reinforcement learning. His work carried out in the field of Artificial intelligence brings together such families of science as Machine learning, Brain–computer interface and Computer vision. His Algorithm research includes themes of Random variable, Variable kernel density estimation, Kalman filter, Similarity measure and Robustness.
He interconnects Hermitian matrix, Convolutional neural network, Matrix, Mutual information and Perceptron in the investigation of issues within Information theory. His Pattern recognition research integrates issues from Time domain, Signal and Interfacing. Jose C. Principe focuses mostly in the field of Reinforcement learning, narrowing it down to topics relating to Value of information and, in certain cases, Mathematical optimization and Markov process.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Neural and Adaptive Systems: Fundamentals through Simulations with CD-ROM
Jose C. Principe;Neil R. Euliano;W. Curt Lefebvre.
Neural and adaptive systems : fundamentals through simulations
J. C. Príncipe;Neil R. Euliano;W. Curt Lefebvre.
Correntropy: Properties and Applications in Non-Gaussian Signal Processing
Weifeng Liu;P.P. Pokharel;J.C. Principe.
IEEE Transactions on Signal Processing (2007)
Information Theoretic Learning: Renyi's Entropy and Kernel Perspectives
Jose C. Principe.
Cooperative Diversity of Spectrum Sensing in Cognitive Radio Networks
Dongliang Duan;Liuqing Yang;Jose C. Principe.
wireless communications and networking conference (2009)
Information Theoretic Learning
Jose C. Principe.
Information Theoretic Learning by Jose C. Principe (2010)
Kernel Adaptive Filtering: A Comprehensive Introduction
Weifeng Liu;Jose C. Principe;Simon Haykin.
Cortical Ensemble Adaptation to Represent Velocity of an Artificial Actuator Controlled by a Brain-Machine Interface
Mikhail A. Lebedev;Jose M. Carmena;Joseph E. O'Doherty;Miriam Zacksenhouse.
The Journal of Neuroscience (2005)
Support vector machines for SAR automatic target recognition
Q. Zhao;J.C. Principe.
IEEE Transactions on Aerospace and Electronic Systems (2001)
Adaptive and Learning Systems for Signal Processing, Communication, and Control
Weifeng Liu;José C. Príncipe;Simon Haykin.
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