This paper summarizes several classes of software cost estimation models and techniques: parametric models, expertise-based techniques, learning-oriented techniques, dynamics-based models, regression-based models, and composite-Bayesian techniques for integrating expertise-based and regression-based models. Experience to date indicates that neural-net and dynamics-based techniques are less mature than the other classes of techniques, but that all classes of techniques are challenged by the rapid pace of change in software technology. The primary conclusion is that no single technique is best for all situations, and that a careful comparison of the results of several approaches is most likely to produce realistic estimates. 1.

Standard statistical practice ignores model uncertainty. Data analysts typically select a model from some class of models and then proceed as if the selected model had generated the data. This approach ignores the uncertainty in model selection, leading to over-con dent inferences and decisions that are more risky than one thinks they are. Bayesian model averaging (BMA) provides a coherent mechanism for accounting for this model uncertainty. Several methods for implementing BMA haverecently emerged. We discuss these methods and present anumber of examples. In these examples, BMA provides improved out-of-sample predictive performance. We also provide a catalogue of

Heuristics are efficient cognitive processes that ignore information. In contrast to the widely held view that less processing reduces accuracy, the study of heuristics shows that less information, computation, and time can in fact improve accuracy. We review the major progress made so far: (a) the discovery of less-is-more effects; (b) the study of the ecological rationality of heuristics, which examines in which environments a given strategy succeeds or fails, and why; (c) an advancement from vague labels to computational models of heuristics; (d) the development of a systematic theory of heuristics that identifies their building blocks and the evolved capacities they exploit, and views the cognitive system as relying on an ‘‘adaptive toolbox;’ ’ and (e) the development of an empirical methodology that accounts for individual differences, conducts competitive tests, and has provided evidence for people’s adaptive use of heuristics. Homo heuristicus has a biased mind and ignores part of the available information, yet a biased mind can handle uncertainty more efficiently and robustly than an unbiased mind relying on more resource-intensive and general-purpose processing strategies.

The paper presents a series of noise detection experiments in a medical problem of coronary artery disease diagnosis. The following algorithms for noise detection and elimination are tested: a saturation filter, a classification filter, a combined classification-saturation filter, and a consensus saturation filter. The distinguishing feature of the novel consensus saturation filter is its high reliability which is due to the multiple detection of potentially noisy examples. Reliable detection of noisy examples is important for the analysis of patient records in medical databases, as well as for the induction of rules from filtered data, representing genuine characteristics of the diagnostic domain. Medical evaluation in the problem of coronary artery disease diagnosis shows that the detected noisy examples are indeed noisy or non-typical class representatives.

A software product is often behind schedule, over budget, non-conforming to requirements and of poor quality. Controlling and improving the processes used to develop software has been proposed as a primary remedy to these problems. The Software Engineering Institute at Carnegie Mellon University has published the Software Capability Maturity Model (SW-CMM) for use as a set of criteria to evaluate an organization's Process Maturity. The model is also used as a roadmap to improve a software development process 's maturity. The premise of the SW-CMM is that mature development processes deliver products on time, within budget, within requirements, and of high quality. This research examines the effects of Software Process Maturity, using the SWCMM, on software development effort. Effort is the primary determinant of software development cost and schedule. The technical challenge in this research is determining how much change in effort is due solely to changing Process Maturity when this change generally occurs concurrently with changes to other factors that also influence software development effort. The six mathematical models used in this research support the following conclusion: For the one hundred twelve projects in this sample, Software Process Maturity was a significant factor (95% confidence level) affecting software development effort. After normalizing for the effects of other effort influences, a one-increment change in the rating of Process Maturity resulted in a 15% to 21% reduction in effort. The modeling approach used in this analysis can be used in other areas of Software Engineering as well.

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1 This article presents a novel methodology for a robot to autonomously induce models of its actions and sensors called asami (Autonomous Sensor and Actuator Model Induction). While previous approaches to model learning rely on an independent source of training data, we show how a robot can induce action and sensor models without any well-calibrated feedback. Specif-ically, the only inputs to the asami learning process are the data the robot would naturally have access to: its raw sensations and knowledge of its own action selections. From the per-spective of developmental robotics, our robot’s goal is to obtain self-consistent internal models, rather than to perform any externally defined tasks. Furthermore, the target function of each model-learning process comes from within the system, namely the most current version of an-other internal system model. Concretely realizing this model-learning methodology presents a number of challenges, and we introduce a broad class of settings in which solutions to these challenges are presented. asami is fully implemented and tested, and empirical results validate our approach in a robotic testbed domain using a Sony Aibo ERS-7 robot.

Mika et al. [1] apply the "kernel trick" to obtain a non-linear variant of Fisher's linear discriminant analysis method, demonstrating state-of-the-art performance on a range of benchmark datasets. We show that leave-one-out cross-validation of kernel Fisher discriminant classifiers can be implemented with a computational complexity of only O(l³) operations rather than the O(l^4) of a nave implementation, where l is the number of training patterns. Leave-one-out cross-validation then becomes an attractive means of model selection in large-scale applications of kernel Fisher discriminant analysis, being significantly faster than conventional k-fold cross-validation procedures commonly used.

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This paper presents a technique for the Simultaneous Calibration of Action and Sensor Models (SCASM) on a mobile robot. While previous approaches to calibration make use of an independent source of feedback, SCASM is unsupervised, in that it does not receive any well calibrated feedback about its location. Starting with only an inaccurate action model, it learns accurate relative action and sensor models. Furthermore, SCASM is fully autonomous, in that it operates with no human supervision. SCASM is fully implemented and tested on a Sony Aibo ERS-7 robot.