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17
Realtime algorithms for statistical analysis of interval data
 Proceedings of the International Conference on Information Technology InTech’03, Chiang Mai
, 2003
"... When we have only interval ranges [x i, xi] of sample values x1,..., xn, what is the interval [V, V] of possible values for the variance V of these values? There are quadratic time algorithms for computing the exact lower bound V on the variance of interval data, and for computing V under reasonable ..."
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Cited by 7 (4 self)
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When we have only interval ranges [x i, xi] of sample values x1,..., xn, what is the interval [V, V] of possible values for the variance V of these values? There are quadratic time algorithms for computing the exact lower bound V on the variance of interval data, and for computing V under reasonable easily verifiable conditions. The problem is that in real life, we often make additional measurements. In traditional statistics, if we have a new measurement result, we can modify the value of variance in constant time. In contrast, previously known algorithms for processing interval data required that, once a new data point is added, we start from the very beginning. In this paper, we describe new algorithms for statistical processing of interval data, algorithms in which adding a data point requires only O(n) computational steps.
Fast Quantum Algorithms for Handling Probabilistic and Interval Uncertainty
, 2003
"... this paper, we show how the use of quantum computing can speed up some computations related to interval and probabilistic uncertainty. We end the paper with speculations on whether (and how) "hypothetic" physical devices can compute NPhard problems faster than in exponential time ..."
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Cited by 7 (7 self)
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this paper, we show how the use of quantum computing can speed up some computations related to interval and probabilistic uncertainty. We end the paper with speculations on whether (and how) "hypothetic" physical devices can compute NPhard problems faster than in exponential time
Probabilities, Intervals, What Next? . . .
 FRONTIERS IN GLOBAL OPTIMIZATION, PP. 12
, 2003
"... When we have only interval ranges [x i ; x i ] of sample values x 1 ; : : : ; xn , what is the interval [V ; V ] of possible values for the variance V of these values? We show that the problem of computing the upper bound V is NPhard. We provide a feasible (quadratic time) algorithm for computin ..."
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Cited by 1 (1 self)
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When we have only interval ranges [x i ; x i ] of sample values x 1 ; : : : ; xn , what is the interval [V ; V ] of possible values for the variance V of these values? We show that the problem of computing the upper bound V is NPhard. We provide a feasible (quadratic time) algorithm for computing the exact lower bound V on the variance of interval data. We also
On Interval Weighted Threelayer Neural Networks
 Proc. of the 31 Annual Simulation Symposium
, 1998
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The Bernstein Basis and its Applications in Solving Geometric Constraint Systems ∗
"... This article reviews the properties of Tensorial Bernstein Basis (TBB) and its usage, with interval analysis, for solving systems of nonlinear, univariate or multivariate equations resulting from geometric constraints. TBB are routinelyused incomputerized geometry for geometric modelling in CADCAM, ..."
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This article reviews the properties of Tensorial Bernstein Basis (TBB) and its usage, with interval analysis, for solving systems of nonlinear, univariate or multivariate equations resulting from geometric constraints. TBB are routinelyused incomputerized geometry for geometric modelling in CADCAM, or in computer graphics. They provide sharp enclosures of polynomials and their derivatives. They are used to reduce domains while preserving roots of polynomial systems, to prove that domains do not contain roots, and to make existence and uniqueness tests. They are compatiblewithstandardpreconditioningmethodsandfitlinearprogramming techniques. However, current Bernsteinbased solvers are limited to small algebraic systems. We present Bernstein polytopes and show how combiningthem with linear programming allows us tosolve larger systems as well. The article also gives a generalization of Bernstein polytopes to higher degrees and a comparison of polytopesbased versus TBBbased polynomial bounds.
The Interval Constructor on classes of MLalgebras. Master’s thesis, Universidade Federal do Rio Grande do Norte
, 2008
"... Dissertation presented to the Program of Post ..."
Constructing Probability Boxes and . . .
, 2003
"... This report summarizes a variety of the most useful and commonly applied methods for obtaining DempsterShafer structures, and their mathematical kin probability boxes, from empirical information or theoretical knowledge. The report includes a review of the aggregation methods for handling agreement ..."
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This report summarizes a variety of the most useful and commonly applied methods for obtaining DempsterShafer structures, and their mathematical kin probability boxes, from empirical information or theoretical knowledge. The report includes a review of the aggregation methods for handling agreement and conflict when multiple such objects are obtained from different sources.
Probabilities, Intervals, What Next? Optimization Problems Related to Extension of Interval Computations to Situations with Partial Information about Probabilities
, 2003
"... When we have only interval ranges [x i ; x i ] of sample values x 1 ; : : : ; xn , what is the interval [V ; V ] of possible values for the variance V of these values? We show that the problem of computing the upper bound V is NPhard. We provide a feasible (quadratic time) algorithm for computin ..."
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When we have only interval ranges [x i ; x i ] of sample values x 1 ; : : : ; xn , what is the interval [V ; V ] of possible values for the variance V of these values? We show that the problem of computing the upper bound V is NPhard. We provide a feasible (quadratic time) algorithm for computing the exact lower bound V on the variance of interval data.
Interval Fuzzy RuleBased Hand Gesture Recognition
"... Abstract. This paper introduces an interval fuzzy rulebased method for the recognition of hand gestures acquired from a data glove, with an application to the recognition of hand gestures of the Brazilian Sign Language. To deal with the uncertainties in the data provided by the data glove, an approa ..."
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Abstract. This paper introduces an interval fuzzy rulebased method for the recognition of hand gestures acquired from a data glove, with an application to the recognition of hand gestures of the Brazilian Sign Language. To deal with the uncertainties in the data provided by the data glove, an approach based on interval fuzzy logic is used. The method uses the set of angles of finger joints and of separation between finger for the classification of hand configurations, and classifications of segments of hand gestures for recognizing gestures. The segmentation of gestures is based on the concept of monotonic gesture segment, sequences of hand configurations in which the variations of the angles of the finger joints have the same sign (nonincreasing or nondecreasing), separated by reference configurations that mark the inflexion points in the sequence. Each gesture is characterized by its list of monotonic segments. The set of all lists of segments of a given set of gestures determines a set of finite automata able to recognize such gestures. 1.