@conference {
	title = {Convergence of Kohonen{\textquoteright}s Learning Vector Quantization},
	author = {Baras, John S., LaVigna, Anthony.},
	year = {1991},
	month = {06/1991},
	abstract = {Kohonen{\textquoteright}s Learning Vector Quantization is a nonparametric classification scheme which classifies observations by comparing then to k; templates called Voronoi vectors. The locations of these vectors are determined from past labeled data through a learning algorithm. When learning is complete, the class of a new observation is the same as the class of the closest Voronoi vector. Hence LVQ is similar to nearest neighbors, except that instead of all of the past observations being searched only the k Voronoi vectors are searched.

In this paper, we show that the LVQ~learning algorithm converges to locally asymptotic stable equilibria of an ordinary differential equation. It is shown that the learning algorithm performs stochastic approximation. Convergence of the vectors is guaranteed under the appropriate conditions on the underlying statistics of the classification problem. We also present a modification to the learning algorithm which results in more robust convergence.},
	address = {San Diego, California},
	booktitle = {The 1990 International Joint Conference on Neural Networks (IJCNN)},
	pages = { pp. 17-20}
}
@conference {
	title = {Convergence of a Neural Network Classifier},
	author = {Baras, John S., LaVigna, Anthony.},
	year = {1990},
	month = {12/1990},
	abstract = {In this paper, we show that the LVQ learning algorithm converges to locally asymptotic stable equilibria of an ordinary differential equation. We show that the learning algorithm performs stochastic approximation. Convergence of the Voronoi vectors is guaranteed under the appropriate conditions on the underlying statistics of the classification problem. We also present a modification to the learning algorithm which we argue results in convergence of the LVQ~for a larger set of initial conditions. Finally, we show that LVQ~is a general histogram classifier and that its risk converges to the Bayesian optimal risk as the appropriate parameters go to infinity with the number~of past observations.},
	address = {Honolulu, Hawaii},
	booktitle = {The 29th Conference on Decision and Control (CDC)},
	pages = {pp. 1735-1740}
}
@conference {
	title = {Convergence of the Vectors in Kohonen{\textquoteright}s Learning Vector Quantization},
	author = {Baras, John S., LaVigna, Anthony.},
	year = {1990},
	month = {07/1990},
	abstract = {Kohonen{\textquoteright}s Learning Vector Quantization is a nonparametric classification scheme which classifies observations by comparing them to k templates called Voronoi vectors. The locations of these vectors are determined from past labeled data through a learning algorithm. When learning is complete, the class of a new observation is the same as the class of the closest Voronoi vector. Hence LVQ is similar to nearest neighbors, except that instead of all of the past observations being searched only the k Voronoi vectors are searched.

In this paper, we show that the LVQ learning algorithm converges to asymptotically stable zeros of an ordinary differential eq{\"u}ation. It is shown that the learning algorithm performs stochastic approximation. Convergence of the vectors is guaranteed under the appropriate conditions on the underlying statistics of the classification problem. We also~present a modification to the learning algorithm which results in more robust convergence.},
	address = {Paris, France},
	booktitle = {The  1990 International Conference on Neural Networks},
	pages = {pp. 1028-1031}
}
@conference {
	title = {Real Time Sequential Detection for Diffusion Signals},
	author = {Baras, John S., LaVigna, Anthony.},
	year = {1987},
	month = {12/1987},
	abstract = {We present the sequential detection problem for diffusion type signals in both the fixed probability of error and the Bayesian formulations. The optimal strategy in both cases is a threshold policy with explicit{\l}y computable thresholds. We provide numerical schemes for approximating the relevant likelihood ratio and~provide an architecture for real-time signal processing.},
	address = {Los Angeles, California},
	booktitle = {The 26th IEEE Conference on Decision and Control},
	pages = {pp. 1153-1157}
}
@conference {
	title = {Architectures for Real-Time Sequential Detection and Estimation},
	author = {Baras, John S., LaVigna, Anthony.},
	year = {1987},
	month = {08/1987},
	abstract = {We present the sequential detection for diffusion type signals both in the fixed probability of error formulation and in the Bayesian formulation. The optimal strategy in both cases is a threshold policy with explicitly computable thresholds. We provide numerical schemes for approximating the revelant likelihood ratio and provide an architecture for real time signal processing.},
	address = {San Diego, CA},
	booktitle = {SPIE{\textquoteright}s 31st Annual International Technical Symposium on Optical and Optoelectronic Applied Science and Engineering, in Real Time Signal Processing X},
	pages = {pp. 100-105}
}
@conference {
	title = {The DELPHI System:  A System Level Tool for Integrated Design of Real-Time Signal Processors},
	author = {Baras, John S., Ebrahimi, Fariborz., Israel, Bruce., LaVigna, Anthony., MacEnany, David C.},
	year = {1987},
	month = {08/1987},
	address = {San Diego, CA},
	booktitle = {SPIE{\textquoteright}s 31st Annual International Technical Symposium on Optical and Optoelectronic Applied Science and Engineering, in Real Time Signal Processing X},
	pages = {pp. 10-14}
}
@conference {
	title = {Architectures for Real-Time Signal Processing},
	author = {Baras, John S., LaVigna, Anthony.},
	year = {1987},
	month = {06/1987},
	abstract = {We present the sequential detection for diffusion type signals both in fixed probability of error formulation and bayesian formulation. The optimal strategy in both cases is a threshold policy with explicitly computable thresholds. We provide numerical schemes for approximating the revelant likelihood ratio and provide an architectural for real time signal processing.},
	address = {Phoenix, Arizona},
	booktitle = {The 1987 International Symposium on Mathematical Theory of Networks and Systems},
	pages = {100-105}
}
@conference {
	title = {A Continuous-Time Distributed Version of Wald{\textquoteright}s Sequential Hypothesis Testing Problem},
	author = {Makowski, Armand M., LaVigna, Anthony., Baras, John S.},
	year = {1986},
	month = {10/1986},
	address = {Ann Arbor, MI},
	booktitle = {The 1986 IEEE International Symposium on Information Theory},
	pages = {pp. 533-543}
}
@conference {
	title = {A Continuous-Time Distributed Version of Wald{\textquoteright}s Sequential Hypothesis Testing Problem},
	author = {LaVigna, Anthony., Makowski, Armand M., Baras, John S.},
	year = {1986},
	month = {06/1986},
	address = {Antibes, France},
	booktitle = {The Seventh International Conference on Analysis and Optimization of Systems},
	pages = {pp. 533-543},
	organization = {INRIA}
}
@conference {
	title = {Architectures for Real-Time Sequential Detection and Estimation},
	author = {Baras, John S., LaVigna, Anthony.},
	year = {1986},
	month = {06/1986},
	abstract = {We present the sequential detection for diffusion type signals in both the fixed probability of error and the Bayesian formulation. The optimal strategy in both cases is a threshold policy with explicit{\l}y computable thresholds. We provide numerical schemes for approximating the relevant likelihood ratio and provide an architecture for real-time signal processing.},
	address = {Minneapolis, Minnesota},
	booktitle = {The Institute for Mathematics and Its Applications Workshop on Stochastic Differential Equations and Their Applications},
	pages = {pp. 100-105}
}
@conference {
	title = {Expert Systems and VLSI Architectures for Non-Gaussian Detection and Nonlinear Filtering},
	author = {Baras, John S., LaVigna, Anthony.},
	year = {1985},
	month = {06/1985},
	address = {Bonn, West Germany},
	booktitle = {The 3rd Bad Honnef Conference on Stochastic Systems}
}
@conference {
	title = {DELPHI: A CAD System for Integrated Design of Non-Gaussian Signal Processors},
	author = {Baras, John S., LaVigna, Anthony.},
	year = {1984},
	month = {12/1984},
	address = {Rome, Italy},
	booktitle = {The IFIP Conference on Recent Advances in Stochastic Estimation and Control}
}
