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If such feedback were available, it might be possible to gain better insight into the types of inputs that could enhance the network effectiveness Several techniques have been developed that attempt to provide an interpretation of the connection weights In one method, the partition is used to make judgments about the relative importance of the various inputs The method consists of applying an equation that effectively partitions the middle-to-output layer weights of each middle (hidden-layer) node into components associated with each input node It has been suggested that this method can make ANNs as effective for modeling applications as for pattern matching A more manually intensive method has been proposed for interpreting the weights that entails iteratively presenting speci c isolated features to the trained network and assessing the network s reaction For example, after training the network to recognize seven letters of the alphabet, various features of the letters (such as the angular top of the letter A) were presented to the network, and the responses of each node were examined Through this method, the discriminating features were identi ed In both the above-mentioned methods, it was unclear to what degree the technique s applicability would extend to networks with different architectures and levels of complexity One of the most interesting methods of a weight-interpreting technique was the Knowledgetron (KT) The KT method uses a sophisticated algorithm to walk through the nodes of the network, interpret the connections, and generate a set of if then rules to match the behavior of the network These rules can then become the basis for a rule-based expert system It is particularly noteworthy that the KT technique was able to process the classic Iris data set successfully A set of only ve relatively simple rules were generated from ANN trained on the Iris data In fact, it was found that the generated rules actually outperformed the ANN from which they were generated Because the Iris data set is not particularly complex, it would be premature to assume that the KT technique would scale to more complex, real-world data sets; however, its performance provides evidence that further research in this area would be worthwhile From the perspective of the data issue, the production of rules provides a convenient mechanism for evaluating the importance and role of each input data element This could form the basis for a feedback-and-re ne iterative process to guide the selection and preprocessing of training data, which might result in improved ANN performance From the foregoing discussion, it can be observed that the range of techniques and possibilities for enhancing ANN effectiveness, even limiting the eld to consider only the data component, is broad and complex Many of these techniques require a level of expertise that may prohibit their use in practical situations; however, a growing body of research and applications in the area of hybrid intelligent systems could address this concern Many hybrid intelligent systems have been developed in recent years Expert systems and ANNs have characteristics that can complement each other and form the basis for powerful hybrid systems Several examples of expert ystems used as.

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Understanding how to implement HA systems well will make you a better overall system administrator, and make you worth ore to your employer, even if you never actually have the chance to roll out a single failover configuration In this book we hope to point out the things that we have learned in implementing hundreds of critical systems in highly available configurations Realistically, it is unlikely that we have hit on every single point that readers will run into while implementing critical systems We do believe, however, that our general advice will be applicable to many specific situations Some readers may begrudge the lack of simple, universal answers in this book There are two reasons for this One is that the issues that arise at each site, and for each computer system, are different.

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It is unreasonable to expect that what works for a 10,000-employee global financial institution will also work for a 10-person law office We offer the choices and allow the reader to determine which one will work best in his or her environment The other reason is that after 15 years of working on, with, and occasionally for computers, I have learned that the most correct answer to most computing problems is a rather unfortunate, It depends We have made the assumption that our readers possess varying technical abilities With rare exceptions, the material in the book is not extremely technical I am not a bits-and-bytes kind of guy (although Hal is), and so I have tried to write the book for other people who are more like me The sections on writing code are a little more bits-and-bytes-oriented, but they are the exception rather than the rule.

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