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In Kumho Tire, Ltd v Carmichael (1999), the Supreme Court considered a question that had bedeviled the lower courts, whether Daubert s gatekeeping obligation extended to so-called non-scienti c expert testimony The Court declared that it did: We conclude that Daubert s general holding setting forth the trial judge s general gatekeeping obligation applies not only to testimony based on scienti c knowledge, but also to testimony based on technical and other specialized knowledge (p 141) The plaintiffs had brought suit after their tire blew out causing an accident in which one person was killed and several others were severely injured Their claim rested largely on the testimony of their expert witness who testi ed in a deposition that the blowout was caused by a defect in the manufacture or design of the tire His testimony was based exclusively on his experience in the tire industry.

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[equation (5.18)] in this smaller case base indicates that (see the last column and last row of Table 5.7) the solution generated from it is almost as accurate as that generated from the original case base (i.e., about 94% accuracy). If more tolerance of the accuracy is allowed in the selection strategy, a substantially smaller case base can be generated. Relative error actual value of the solution computed value 100% 5:18 actual value of the solution

FIGURE 11.42 Using the Mirror tool, drag up on the left side and down on the right side. Pixels are reflected in a clockwise direction.

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He had conducted no tests on the tire in question or on similarly situated tires He provided no statistical information linking the factors he identi ed as being indicative of tire failures to a manufacturing defect The defendant moved to exclude the expert s testimony and for summary judgment It claimed that the plaintiff s expert s testimony failed the Daubert standard, for it was not based on tested research, had no known error rate, had not been published in peer-reviewed journals, and was not generally accepted among engineers The trial court agreed that the plaintiff s engineer failed the Daubert test Plaintiffs asked for reconsideration, arguing that the court had applied the Daubert factors too in exibly The court concluded that, however exibly it applied the Daubert test, the plaintiffs expert s testimony was not suf ciently reliable to allow On appeal, the Eleventh Circuit Court of Appeals reversed.

Implementation Methods Corrector Method Kalman [71], Joseph [19] Potter [100, 8] Carlson [20] Predictor Method Kalman [71] Kalman [71] Ck 1 Fk Ck

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The Circuit Court applied a de novo standard to the district court s legal decision to apply Daubert (Carmichael v Samyang Tire, Inc (1997)) It then went on to nd that the Daubert test was limited to scienti c knowledge , and that since the plaintiffs expert based his testimony on his experience and not the scienti c method, it had to remand for a different admissibility analysis The Supreme Court reversed Justice Breyer, writing for the Court, stated that the Daubert gatekeeping function applies to all expert testimony The Court explained.

The preliminary steps mainly serve the purpose to decide whether the main steps should be carried out. Only if the potential benefit is high enough and the demands can be met by data mining methods, can it be expected that some profit results from the usually expensive main steps. In the main steps the data to be analyzed for hidden knowledge are first collected (if necessary), appropriate subsets are selected, and they are transformed into a unique format that is suitable for applying data mining techniques. Then they are cleaned and reduced to improve the performance of the algorithms to be applied later. These preprocessing steps usually consume the greater part of the total costs. Depending on the data mining task that was identified in the goal definition step (see below for a list), data mining methods are applied (see farther below for a list), the results of which, in order to interpret and evaluate them, can be visualized. Since the desired goal is rarely achieved in the first go, usually several steps of the preprocessing phase (for example feature selection) and the application of data mining methods have to be reiterated in order to improve the result. If it has not been obvious before, it is clear now that KDD is an interactive process, rather than completely automated. A user has to evaluate the results, check them for plausibility, and test them against hold-out data. If necessary, he/she modifies the course of the process to make it meet his/her requirements.

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