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On the optimality of the simple Bayesian classifier under zeroone loss
 MACHINE LEARNING
, 1997
"... The simple Bayesian classifier is known to be optimal when attributes are independent given the class, but the question of whether other sufficient conditions for its optimality exist has so far not been explored. Empirical results showing that it performs surprisingly well in many domains containin ..."
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Cited by 805 (26 self)
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containing clear attribute dependences suggest that the answer to this question may be positive. This article shows that, although the Bayesian classifier’s probability estimates are only optimal under quadratic loss if the independence assumption holds, the classifier itself can be optimal under zero
CONVERGENCE TO FRACTIONAL BROWNIAN MOTION AND LOSS PROBABILITY
"... Abstract. We study the weak convergence to Fractional Brownian motion and some examples with applications to traffic modeling. Finally, we get loss probability for queuelength distribution related to selfsimilar process. 1. ..."
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Abstract. We study the weak convergence to Fractional Brownian motion and some examples with applications to traffic modeling. Finally, we get loss probability for queuelength distribution related to selfsimilar process. 1.
BOUNDS FOR THE LOSS PROBABILITIES OF LARGE LOSS QUEUEING SYSTEMS
, 804
"... Abstract. The aim of this paper is to establish the bounds for the least root of the functional equation x = b G(µ − µx), where b G(s) is the LaplaceStieltjes transform of an unknown probability distribution function G(x) of a positive random variable having the first two moments g1 and g2, and µ i ..."
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Cited by 2 (2 self)
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bounds for the positive least root of the functional equation x = b G(µ − µx) are then used to find the asymptotic bounds for the loss probabilities in certain queueing systems with a large number of waiting places, when only an empirical probability distribution function of an interarrival or service
The performance of TCP/IP for networks with high bandwidthdelay products and random loss
, 1997
"... This paper examines the performance of TCP/IP, the Internet data transport protocol, over Wide Area Networks (WANs) in which data traffic could coexist with realtime traffic such as voice and video. Specifically, we attempt to develop a basic understanding, using analysis and simulation, of the pro ..."
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Cited by 464 (6 self)
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of interest. The following key results are obtained. First, random loss leads to significant throughput deterioration when the product of the loss probability and the square of the bandwidthdelay product is larger than one. Unless network resources are specifically reserved for data traffic, data traffic
On Loss Probabilities in Presence of Redundant Packets with Random Drop
"... The purpose of this paper is to study the loss probabilities of messages in an M/M/1/K queueing system where in addition to losses due to buffer overflow there are also random losses in the incoming and outgoing links. We focus on the influence of adding redundant packets to the messages (as in e ..."
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Cited by 24 (6 self)
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The purpose of this paper is to study the loss probabilities of messages in an M/M/1/K queueing system where in addition to losses due to buffer overflow there are also random losses in the incoming and outgoing links. We focus on the influence of adding redundant packets to the messages (as
MINIMIZING LOSS PROBABILITY IN QUEUING SYSTEMS WITH HETEROGENEOUS SERVERS*
"... Abstract – The probability of losing a customer in M/G/n/0 and GI/M/n/0 loss queuing systems with heterogeneous servers is minimized. The first system uses a queue discipline in which a customer who arrives when there are free servers chooses any one of them with equal probability, but is lost other ..."
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Abstract – The probability of losing a customer in M/G/n/0 and GI/M/n/0 loss queuing systems with heterogeneous servers is minimized. The first system uses a queue discipline in which a customer who arrives when there are free servers chooses any one of them with equal probability, but is lost
Estimating the Loss Probability for Finite Buffer Multiplexers
"... In this paper we propose an approximation for the loss probability in a finite buffer system by making a simple mapping from the tail probability in an infinite buffer system. We focus on the case of a highspeed network where a large number of sources are expected to be multiplexed so that we model ..."
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In this paper we propose an approximation for the loss probability in a finite buffer system by making a simple mapping from the tail probability in an infinite buffer system. We focus on the case of a highspeed network where a large number of sources are expected to be multiplexed so that we
Gradient flows in metric spaces and in the space of probability measures
 LECTURES IN MATHEMATICS ETH ZÜRICH, BIRKHÄUSER VERLAG
, 2005
"... ..."
Resilient Overlay Networks
, 2001
"... A Resilient Overlay Network (RON) is an architecture that allows distributed Internet applications to detect and recover from path outages and periods of degraded performance within several seconds, improving over today’s widearea routing protocols that take at least several minutes to recover. A R ..."
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Cited by 1163 (34 self)
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in the Internet. Furthermore, RON was able to improve the loss rate, latency, or throughput perceived by data transfers; for example, about 5 % of the transfers doubled their TCP throughput and 5 % of our transfers saw their loss probability reduced by 0.05. We found that forwarding packets via at most one
Advances in Prospect Theory: Cumulative Representation of Uncertainty
 JOURNAL OF RISK AND UNCERTAINTY, 5:297323 (1992)
, 1992
"... We develop a new version of prospect theory that employs cumulative rather than separable decision weights and extends the theory in several respects. This version, called cumulative prospect theory, applies to uncertain as well as to risky prospects with any number of outcomes, and it allows differ ..."
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Cited by 1603 (12 self)
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distinctive fourfold pattern of risk attitudes: risk aversion for gains and risk seeking for losses of high probability; risk seeking for gains and risk aversion for losses of low probability.
Results 1  10
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