Bayesian Active Learning for Radiation Pattern Sampling Over Cylindrical Surfaces

Sampling for Bayesian Program Learning

Towards learning programs from data, we introduce the problem of sampling programs from posterior distributions conditioned on that data. Within this setting, we propose an algorithm that uses a symbolic solver to efficiently sample programs. The proposal combines constraint-based program synthesis with sampling via random parity constraints. We give theoretical guarantees on how well the sampl...

On-line learning in pattern classification using active sampling

An adaptive on-line learning method is presented to faciliate pattern classi cation using active sampling to identify optimal decision boundary for a stochastic oracle with minimum number of training samples. The strategy of sampling at the current estimate of the decision boundary is shown to be optimal in the sense that the probability of convergence toward the true decision boundary at each ...

Dual Polarization Synthesis and Optimization of Cylindrical Offset Reflector Antenna for Cosecant-squared Radiation Pattern

This paper presents the shaping optimization of an S-band cylindrical offset reflector antenna with cosecant-squared radiation pattern in elevation plane. The cross section of the antenna is mathematically modeled by a polynomial function and optimized by the genetic algorithm to obtain the desired pattern for both TE and TM polarizations. The physical optics method together with the uniform th...

Supplement to: Sampling for Bayesian Program Learning

Adaptive Informative Sampling for Active Learning

Many approaches to active learning involve periodically training one classifier and choosing data points with the lowest confidence. An alternative approach is to periodically choose data instances that maximize disagreement among the label predictions across an ensemble of classifiers. Many classifiers with different underlying structures could fit this framework, but some ensembles are more s...