Results 1  10
of
238,399
ContinuousTime Stochastic Input Behavior
, 2012
"... Dynamic predictive modeling under measured and unmeasured continuoustime stochastic input behavior ..."
Abstract
 Add to MetaCart
Dynamic predictive modeling under measured and unmeasured continuoustime stochastic input behavior
Learning with stochastic inputs and adversarial outputs
 Journal of Computer and System Sciences
, 2011
"... Most of the research in online learning is focused either on the problem of adversarial classification (i.e., both inputs and labels are arbitrarily chosen by an adversary) or on the traditional supervised learning problem in which samples are independent and identically distributed according to a s ..."
Abstract

Cited by 4 (0 self)
 Add to MetaCart
stationary probability distribution. Nonetheless, in a number of domains the relationship between inputs and outputs may be adversarial, whereas input instances are i.i.d. from a stationary distribution (e.g., user preferences). This scenario can be formalized as a learning problem with stochastic inputs
Language learning from stochastic input
 In REFERENCES 51 Proceedings of the fifth conference on Computational Learning Theory
, 1992
"... Language learning from positive data in the Gold model of inductive inference is investigated in a setting where the data can be modeled as a stochastic process. Specifically, the input strings are assumed to form a sequence of identically distributed, independent random variables, where the distr ..."
Abstract

Cited by 9 (2 self)
 Add to MetaCart
Language learning from positive data in the Gold model of inductive inference is investigated in a setting where the data can be modeled as a stochastic process. Specifically, the input strings are assumed to form a sequence of identically distributed, independent random variables, where
Contour Tracking By Stochastic Propagation of Conditional Density
, 1996
"... . In Proc. European Conf. Computer Vision, 1996, pp. 343356, Cambridge, UK The problem of tracking curves in dense visual clutter is a challenging one. Trackers based on Kalman filters are of limited use; because they are based on Gaussian densities which are unimodal, they cannot represent s ..."
Abstract

Cited by 658 (24 self)
 Add to MetaCart
simultaneous alternative hypotheses. Extensions to the Kalman filter to handle multiple data associations work satisfactorily in the simple case of point targets, but do not extend naturally to continuous curves. A new, stochastic algorithm is proposed here, the Condensation algorithm  Conditional
Stochastic Inversion Transduction Grammars and Bilingual Parsing of Parallel Corpora
, 1997
"... ..."
Model discrimination of polynomial systems via stochastic inputs
 in Proc. IEEE Conf. on Decision and Control
, 2008
"... Abstract—Systems biologists are often faced with competing models for a given experimental system. Unfortunately, performing experiments can be timeconsuming and expensive. Therefore, a method for designing experiments that, with high probability, discriminate between competing models is desired. ..."
Abstract

Cited by 4 (3 self)
 Add to MetaCart
Abstract—Systems biologists are often faced with competing models for a given experimental system. Unfortunately, performing experiments can be timeconsuming and expensive. Therefore, a method for designing experiments that, with high probability, discriminate between competing models is desired. In particular, biologists often employ models comprised of polynomial ordinary differential equations that arise from biochemical networks. Within this setting, the discrimination problem is cast as a finitehorizon, dynamic, zerosum game in which parameter uncertainties in the model oppose the effort of the experimental conditions. The resulting problem, including some of its known relaxations, is intractable in general. Here, a new scalable relaxation method that yields sufficient conditions for discrimination is developed. If the conditions are met, the method also computes the associated random experiment that can discriminate between competing models with high probability, regardless of the actual system behavior. The method is illustrated on a biochemical network with an unknown structure. I.
The WienerAskey Polynomial Chaos for Stochastic Differential Equations
 SIAM J. SCI. COMPUT
, 2002
"... We present a new method for solving stochastic differential equations based on Galerkin projections and extensions of Wiener's polynomial chaos. Specifically, we represent the stochastic processes with an optimum trial basis from the Askey family of orthogonal polynomials that reduces the dime ..."
Abstract

Cited by 370 (38 self)
 Add to MetaCart
the dimensionality of the system and leads to exponential convergence of the error. Several continuous and discrete processes are treated, and numerical examples show substantial speedup compared to MonteCarlo simulations for low dimensional stochastic inputs.
Developing a Stochastic Input Oriented Data Envelopment Analysis (SIODEA) Model
"... Abstract—Data Envelopment Analysis (DEA) is a powerful quantitative tool that provides a means to obtain useful information about efficiency and performance of firms, organizations, and all sorts of functionally similar, relatively autonomous operating units, known as Decision Making Units (DMU). Us ..."
Abstract

Cited by 1 (1 self)
 Add to MetaCart
). Usually the investigated DMUs are characterized by a vector of multiple inputs and multiple outputs. Unfortunately, not all inputs and/or outputs are deterministic; some could be stochastic. The main concern in this paper is to develop an algorithm to help any organization for evaluating their performance
Results 1  10
of
238,399