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represents a typical nearest-neighbor technique that describes a situation for which p and q are two cases compared for similarity, n is the number of attributes in each case, i is an individual attribute from 1 to n, and wi is the feature weight of attribute i. Generally, the similarity calculation continues until all cases in the case library have been compared, and ranked according to their similarity to a target problem case. Similarities are usually normalized to fall within the range 0 to 1, where 1 means a perfect match and 0 indicates a total mismatch. Methods other than nearest-neighbor techniques have also been used to determine similarity among cases (e.g., induction techniques such as ID3 and C4.5, fuzzy preference functions, and database techniques such as structured query language). Since CBR is a methodology rather than a technology, inductions and many clustering algorithms, such as c-means clustering [26], Kohonen s self-organized network [27], and Fu s similarity matrix [28], could be used to partition a case library for similarity assessment. These techniques generally use three indexes as a measure of the clustering performance: intercluster similarity, intracluster similarity, and the total number of clusters. Many powerful commercial CBR tools have provided this function (e.g., Kate from AcknoSoft, ReCall from ISoft, CBR-Works from TecInno, and ReMind from Cognitive Systems) [29]. As suggested by Watson [25], if CBR is considered to be a methodology, this has a profound implication: CBR has no technology to call its own and therefore must use other technologies. It is this CBR has no technology scenario that creates a challenge for CBR researchers. Any number of technologies or approaches can be used and applied to the CBR cycle. Consequently, it is as a methodology that CBR s future is assured. 1.11 SOFT CASE-BASED REASONING

At the half-neutralization point, however, this relation does not hold and b is expressed as follows, if Kf(HA )(Ca + Cs) >> 1: 2 b1/2 = (2.3/2) {(Ca + Cs)/2Kf(HA )}1/2 2 The buffer capacities for various Kf (HA ) 2 (Ca + Cs) values are shown in Fig. 3.4 against the values of (paH pKa log c), which is shown on the ordinate in Fig. 3.3.

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diting images in Photoshop is done by applying a series of adjustments to modify the original pixels in the image. Each adjustment builds on the others until you achieve the desired results. Photoshop provides two powerful features that allow you to be more productive by utilizing and manipulating the sets of adjustments made to images. The history feature tracks each change to the individual image. This allows you to have a record of each state of the image during editing. Photoshop provides several highly useful tools that allow you to make dynamic adjustments to the image by manipulating the history states. These tools also allow you to play around with different techniques because you can quickly revert back to previous states or remove adjustments that do not work well. The actions feature allows you to bundle a set of adjustments as an individual action. You can save actions to a file and then use them in other images. This feature saves lots of time when you are performing similar adjustments to images.

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Deterministic Problem Now consider instead deterministic variables {x,y} and a data matrix H relating them via

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