10,743 research outputs found
The Adaptive Sampling Revisited
The problem of estimating the number of distinct keys of a large
collection of data is well known in computer science. A classical algorithm
is the adaptive sampling (AS). can be estimated by , where is
the final bucket (cache) size and is the final depth at the end of the
process. Several new interesting questions can be asked about AS (some of them
were suggested by P.Flajolet and popularized by J.Lumbroso). The distribution
of is known, we rederive this distribution in a simpler way.
We provide new results on the moments of and . We also analyze the final
cache size distribution. We consider colored keys: assume that among the
distinct keys, do have color . We show how to estimate
. We also study colored keys with some multiplicity given by
some distribution function. We want to estimate mean an variance of this
distribution. Finally, we consider the case where neither colors nor
multiplicities are known. There we want to estimate the related parameters. An
appendix is devoted to the case where the hashing function provides bits with
probability different from
Adaptive sampling by information maximization
The investigation of input-output systems often requires a sophisticated
choice of test inputs to make best use of limited experimental time. Here we
present an iterative algorithm that continuously adjusts an ensemble of test
inputs online, subject to the data already acquired about the system under
study. The algorithm focuses the input ensemble by maximizing the mutual
information between input and output. We apply the algorithm to simulated
neurophysiological experiments and show that it serves to extract the ensemble
of stimuli that a given neural system ``expects'' as a result of its natural
history.Comment: 4 pages, 2 figure
Self-organizing map based adaptive sampling
We propose a new adaptive sampling method that uses Self-Organizing Maps (SOM). In SOM, densely sampled regions in the input space is represented by a larger area on the map than that of sparsely sampled regions. We use this property to progressively tune-in on the interesting region of the design space. The method does not rely on parameterized distribution, and can sample from multi-modal and non-convex distributions. In this paper, we minimize several mathematical test functions. We also show its performance in inequality-constrained objective satisfaction problem, in which the objective is to seek diversity in solutions satisfying certain upper-bound constraint in the minimized objective. A new merit function and a measure of space-filling quality were proposed for this purpose
ADAPTIVE SAMPLING
This paper introduces a new method for signal representation. It is shown that a periodic signal is uniquely defined by its local extrema if the band limit ratio of the signal is less than an octave. A way of adaptive sampling, introduced among these lines, exhibits advantageous properties of possible interest, e.g., for the detection of the pitch frequency
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