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A Validation Of ObjectOriented Design Metrics As Quality Indicators
 IEEE Transactions on Software Engineering
, 1995
"... This paper presents the results of a study conducted at the University of Maryland in which we experimentally investigated the suite of ObjectOriented (OO) design metrics introduced by [Chidamber&Kemerer, 1994]. In order to do this, we assessed these metrics as predictors of faultprone clas ..."
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Cited by 431 (19 self)
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This paper presents the results of a study conducted at the University of Maryland in which we experimentally investigated the suite of ObjectOriented (OO) design metrics introduced by [Chidamber&Kemerer, 1994]. In order to do this, we assessed these metrics as predictors of faultprone
Analysis of CK Metrics to predict Software FaultProneness using Bayesian Inference
"... The fault prediction model grants assistance during the software development by providing recourse to the present faults with the Bayesian Interference. All faults prediction techniques get a help in this study with the designing of Logistic regression model and Bayesian inference altogether. It is ..."
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that there is a relationship between faulty classes and objectoriented metrics. This study demonstrates as the performance evaluation technique for any piece of software. We examine the open source Eclipse system, which has a strong industrial usage. The focus of the study is to design Bayesian Inference graph
Ptolemy: A Framework for Simulating and Prototyping Heterogeneous Systems
, 1992
"... Ptolemy is an environment for simulation and prototyping of heterogeneous systems. It uses modern objectoriented software technology (C++) to model each subsystem in a natural and efficient manner, and to integrate these subsystems into a whole. Ptolemy encompasses practically all aspects of design ..."
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Cited by 569 (90 self)
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Ptolemy is an environment for simulation and prototyping of heterogeneous systems. It uses modern objectoriented software technology (C++) to model each subsystem in a natural and efficient manner, and to integrate these subsystems into a whole. Ptolemy encompasses practically all aspects
Bro: A System for Detecting Network Intruders in RealTime
, 1999
"... We describe Bro, a standalone system for detecting network intruders in realtime by passively monitoring a network link over which the intruder's traffic transits. We give an overview of the system's design, which emphasizes highspeed (FDDIrate) monitoring, realtime notification, clear ..."
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Cited by 903 (41 self)
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We describe Bro, a standalone system for detecting network intruders in realtime by passively monitoring a network link over which the intruder's traffic transits. We give an overview of the system's design, which emphasizes highspeed (FDDIrate) monitoring, realtime notification
Toward the next generation of recommender systems: A survey of the stateoftheart and possible extensions
 IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
, 2005
"... This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: contentbased, collaborative, and hybrid recommendation approaches. This paper also describes vario ..."
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Cited by 1420 (21 self)
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This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: contentbased, collaborative, and hybrid recommendation approaches. This paper also describes
Factor Graphs and the SumProduct Algorithm
 IEEE TRANSACTIONS ON INFORMATION THEORY
, 1998
"... A factor graph is a bipartite graph that expresses how a "global" function of many variables factors into a product of "local" functions. Factor graphs subsume many other graphical models including Bayesian networks, Markov random fields, and Tanner graphs. Following one simple c ..."
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Cited by 1787 (72 self)
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A factor graph is a bipartite graph that expresses how a "global" function of many variables factors into a product of "local" functions. Factor graphs subsume many other graphical models including Bayesian networks, Markov random fields, and Tanner graphs. Following one simple
Assessing the Applicability of FaultProneness Models Across ObjectOriented Software Projects
"... A number of papers have investigated the relationships between design metrics and the detection of faults in objectoriented software. Several of these studies have shown that such models can be accurate in predicting faulty classes within one particular software product. In practice, however, predic ..."
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Cited by 66 (2 self)
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, because of system differences, the predicted fault probabilities are not representative of the system predicted. Furthermore, a costbenefit model demonstrates that the MARS faultproneness model is potentially viable, from an economical standpoint. The linear model is not, thus suggesting a more complex
Robust Monte Carlo Localization for Mobile Robots
, 2001
"... Mobile robot localization is the problem of determining a robot's pose from sensor data. This article presents a family of probabilistic localization algorithms known as Monte Carlo Localization (MCL). MCL algorithms represent a robot's belief by a set of weighted hypotheses (samples), whi ..."
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Cited by 826 (88 self)
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), which approximate the posterior under a common Bayesian formulation of the localization problem. Building on the basic MCL algorithm, this article develops a more robust algorithm called MixtureMCL, which integrates two complimentary ways of generating samples in the estimation. To apply this algorithm
Locally weighted learning
 ARTIFICIAL INTELLIGENCE REVIEW
, 1997
"... This paper surveys locally weighted learning, a form of lazy learning and memorybased learning, and focuses on locally weighted linear regression. The survey discusses distance functions, smoothing parameters, weighting functions, local model structures, regularization of the estimates and bias, ass ..."
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Cited by 594 (53 self)
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, assessing predictions, handling noisy data and outliers, improving the quality of predictions by tuning t parameters, interference between old and new data, implementing locally weighted learning e ciently, and applications of locally weighted learning. A companion paper surveys how locally weighted
Results 1  10
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