A. Kuh

发表

Learning Time-Varying Concepts

Ronald L. Rivest, Anthony Kuh, Thomas Petsche, 1990, NIPS.

A Belief Propagation Based Power Distribution System State Estimator

Tao Yang, Anthony Kuh, Aleksandar Kavcic, 2011, IEEE Computational Intelligence Magazine.

Micro-grid State Estimation Using Belief Propagation on Factor Graphs

A. Kuh, A. Kavcic, Ying Hu, 2011 .

Performance Bounds for Single Layer Threshold Networks when Tracking a Drifting Adversary

Anthony Kuh, Xiaodong Tian, A. Kuh, 1997, Neural Networks.

Support vector machine for multiuser detection in CDMA communications

A. Kuh, Xiaohong Gong, 1999, Conference Record of the Thirty-Third Asilomar Conference on Signals, Systems, and Computers (Cat. No.CH37020).

Bad Data Detection Using Kernel Density Estimation

A. Kuh, M. Ilić, M. Uddin, 2014 .

Grid Monitoring : Bounds on Performances of Sensor Placement Algorithms

A. Kuh, A. Kavcic, M. Uddin, 2013 .

On-line tracking abilities of neural networks with graded responses

A. Kuh, 1998, ISCAS '98. Proceedings of the 1998 IEEE International Symposium on Circuits and Systems (Cat. No.98CH36187).

Mean squared error analysis of analog neural networks subject to drifting targets and noise

A. Kuh, 1998, Conference Record of Thirty-Second Asilomar Conference on Signals, Systems and Computers (Cat. No.98CH36284).

Wind prediction using complex augmented adaptive filters

Anthony Kuh, Nathan Kowahl, A. Kuh, 2010, The 2010 International Conference on Green Circuits and Systems.

Covariance selection quality through detection problem and AUC bounds

N. T. Khajavi, A. Kuh, 2018 .

Adaptive selective kernel learning algorithms

A. Kuh, 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

A THESIS SUBMITTED TO THE GRADUATE DIVISION OF THE UNIVERSITY OF HAWAI'I IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE IN ELECTRICAL ENGINEERING

Jin W. Chang, A. Kuh, 2006 .

Real Time Kernel Learning for Sensor Networks using Principles of Federated Learning

A. Kuh, 2021, 2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC).

Personalized Learning using Multiple Kernel Models

A. Kuh, Cynthia Chen, Shuai Huang, 2021, 2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC).

Personalized Learning Using Kernel Methods and Random Fourier Features

Clyde James Felix, A. Kuh, Japhet Ye, 2022, 2022 International Joint Conference on Neural Networks (IJCNN).

Unsupervised learning applied to image coding

Anthony Kuh, Meina Xu, A. Kuh, 1995, Proceedings of ISCAS'95 - International Symposium on Circuits and Systems.

Nonlinear Adaptive Filtering With Kernel Set-Membership Approach

Yih-Fang Huang, Anthony Kuh, Stefan Werner, 2020, IEEE Transactions on Signal Processing.

Online and batch methods for solar radiation forecast under asymmetric cost functions

Anthony Kuh, Matthias Fripp, Seyyed A. Fatemi, 2016 .

Online solar radiation forecasting under asymmetrie cost functions

Anthony Kuh, Matthias Fripp, Seyyed A. Fatemi, 2014, Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2014 Asia-Pacific.

A robust general normalised gradient descent algorithm

A. Kuh, D. Obradovic, A. Kuh, 2005, IEEE/SP 13th Workshop on Statistical Signal Processing, 2005.

Stochastic queuing models for distributed PV energy

Chuanyi Ji, Yun Wei, Anthony Kuh, 2012, Proceedings of The 2012 Asia Pacific Signal and Information Processing Association Annual Summit and Conference.

The Quality of the Covariance Selection Through Detection Problem and AUC Bounds pdf

Navid Tafaghodi Khajavi, Anthony Kuh, N. T. Khajavi, 2016, ArXiv.

Nested performance bounds and approximate solutions for the sensor placement problem

Toshihisa Tanaka, Anthony Kuh, Aleksandar Kavcic, 2014, APSIPA Transactions on Signal and Information Processing.

J-STSP Senior Editorial Board

G. Giannakis, Lina Karam, U. Hawaii, 2015 .

Approximate solutions and performance bounds for the sensor placement problem

Toshihisa Tanaka, Anthony Kuh, Aleksandar Kavcic, 2012, 2012 IEEE Third International Conference on Smart Grid Communications (SmartGridComm).

Parametric methods for probabilistic forecasting of solar irradiance

Anthony Kuh, Matthias Fripp, Seyyed A. Fatemi, 2018, Renewable Energy.

Real-time state estimation on micro-grids

Anthony Kuh, Aleksandar Kavcic, Ying Hu, 2011, The 2011 International Joint Conference on Neural Networks.

Image coding using feature map finite-state vector quantization

A. Kuh, Meina Xu, 1996, IEEE Signal Processing Letters.

Complex-valued prediction of wind profile using augmented complex statistics

Kazuyuki Aihara, Danilo P. Mandic, Anthony Kuh, 2009 .

Communication-Efficient Online Federated Learning Framework for Nonlinear Regression pdf

Anthony Kuh, Vinay Chakravarthi Gogineni, Stefan Werner, 2021, IEEE International Conference on Acoustics, Speech, and Signal Processing.

Asynchronous Online Federated Learning with Reduced Communication Requirements

Vinay Chakravarthi Gogineni, A. Kuh, François Gauthier, 2023, IEEE Internet of Things Journal.

A smart algorithm for incremental learning

A. Kuh, E.H.-C. Wang, A. Kuh, 1992, [Proceedings 1992] IJCNN International Joint Conference on Neural Networks.

Hybrid neural network and pattern classification learning algorithms

A. Mathur, A. Kuh, G. Iseri, 1990, IEEE International Symposium on Circuits and Systems.

Information capacity of associative memories

Anthony Kuh, Bradley W. Dickinson, B. Dickinson, 1989, IEEE Trans. Inf. Theory.

Signal Processing Techniques for Knowledge Extraction and Information Fusion

Toshihisa Tanaka, Danilo P. Mandic, Anthony Kuh, 2008 .

Communication-Efficient Online Federated Learning Strategies for Kernel Regression

Vinay Chakravarthi Gogineni, A. Kuh, Yih-Fang Huang, 2023, IEEE Internet of Things Journal.

Clustered Graph Federated Personalized Learning

Vinay Chakravarthi Gogineni, A. Kuh, Yih-Fang Huang, 2022, Asilomar Conference on Signals, Systems and Computers.

Personalized Graph Federated Learning with Differential Privacy

Vinay Chakravarthi Gogineni, A. Kuh, François Gauthier, 2023, 2306.06399.

Personalized Online Federated Learning for IoT/CPS: Challenges and Future Directions

Vinay Chakravarthi Gogineni, A. Kuh, François Gauthier, 2022, IEEE Internet of Things Magazine.

Decentralized Graph Federated Multitask Learning for Streaming Data

Vinay Chakravarthi Gogineni, A. Kuh, Yih-Fang Huang, 2022, 2022 56th Annual Conference on Information Sciences and Systems (CISS).

Resource-Aware Asynchronous Online Federated Learning for Nonlinear Regression pdf

Anthony Kuh, Vinay Chakravarthi Gogineni, Stefan Werner, 2021, ICC 2022 - IEEE International Conference on Communications.

Performance of analog neural networks subject to drifting targets and noise

Anthony Kuh, A. Kuh, 1999, IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339).

Comparison of tracking algorithms for single layer threshold networks in the presence of random drift

Anthony Kuh, A. Kuh, 1997, IEEE Trans. Signal Process..

Tracking capabilities of single-layer threshold networks when confronted with drift and random noise

A. Kuh, Xiaodong Tian, A. Kuh, 1996, 1996 IEEE International Symposium on Circuits and Systems. Circuits and Systems Connecting the World. ISCAS 96.

Learning in time varying environments

Anthony Kuh, Thomas Petsche, T. Petsche, 2000 .

Towards Qualitative Assessment of Machine Learning Algorithms: Utilising Signal Modality Characterisation

T. Gautama, Mo Chen, A. Kuh, 2006, 2006 16th IEEE Signal Processing Society Workshop on Machine Learning for Signal Processing.

Transforming the Engineering Curriculum: Lessons Learned from a Summer at Boeing

Michael E. Gorman, Shankar P. Bhattacharyya, Edwin R. Schmeckpeper, 2001 .

Energy efficient scheduling algorithms for pumping water in radial networks

Vijay Gupta, Anthony Kuh, Seyyed A. Fatemi, 2016, 2016 Information Theory and Applications Workshop (ITA).

Adaptive least square kernel algorithms and applications

Anthony Kuh, A. Kuh, 2002, Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290).

Analysis of detectors for support vector machines and least square support vector machines

Anthony Kuh, A. Kuh, 2002, Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290).

Adaptive kernel methods for CDMA systems

Anthony Kuh, A. Kuh, 2001, IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222).

Least Squares Kernel Methods and Applications

Anthony Kuh, A. Kuh, 2004 .

Online bad data detection using kernel density estimation

Yang Weng, Marija Ilic, Anthony Kuh, 2015, 2015 IEEE Power & Energy Society General Meeting.

Localization of nodes and personnel in a multistatic radar sensor network

Olga Boric-Lubecke, Anthony Kuh, Anders Host-Madsen, 2007, SPIE Defense + Commercial Sensing.

Online unsupervised kernel learning algorithms

Anthony Kuh, Muhammad Sharif Uddin, Phyllis Ng, 2017, 2017 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC).

Comments on "Pruning Error Minimization in Least Squares Support Vector Machines"

Anthony Kuh, Philippe De Wilde, A. Kuh, 2007, IEEE Trans. Neural Networks.

Sensor Network Localization Using Least Squares Kernel Regression

Danilo P. Mandic, Anthony Kuh, Chaopin Zhu, 2006, KES.

Approximate dynamic programming for control of a residential water heater

Anthony Kuh, Matthias Fripp, Matthew Motoki, 2015, 2015 International Joint Conference on Neural Networks (IJCNN).

Signal and information processing applications for the smart grid

Anthony Kuh, A. Kuh, 2015, 2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA).

Classification of humpback whale vocalizations using a self-organizing neural network

Eduardo Mercado, A. Kuh, A. Kuh, 1998, 1998 IEEE International Joint Conference on Neural Networks Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36227).

Temporal difference learning applied to sequential detection

Anthony Kuh, Chengan Guo, A. Kuh, 1997, IEEE Trans. Neural Networks.

A learning sequential detection method based on neural networks

Chengan Guo, A. Kuh, A. Kuh, 1996, Proceedings of Third International Conference on Signal Processing (ICSP'96).

Data Fusion for Modern Engineering Applications: An Overview

Anthony G. Constantinides, Danilo P. Mandic, Tülay Adali, 2005, ICANN.