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So why is too much steel produced The market equilibrium is where the demand curve, D, crosses the Private MC curve. That equilibrium results in a quantity qm of steel being produced, where m stands for market.

Mareels (2004) MaxNet: Faster Flow Control Convergence, in Networking 2004, Springer Lecture Notes in Computer Science 3042, 588 599, Greece, 2004 [17] B Wydrowski, LLH Andrew, and M Zukerman (2003) MaxNet: A Congestion Control Architecture for Scalable Networks, IEEE Communications Letters, 7, 511 513 [18] D Katabi and M Handley (2001) Using Precise Feedback for Controlling Congestion in the Internet , MIT-LCS Technical Report 820..

ARQ handling TX power and buffer status feedback generation and transmission Multicode transmission Uplink scheduling

In Sections 33 and 34 we have brie y described some similarity measures and then introduced the concept of fuzzy similarity, which are closely based on the feature weights Essentially, feature weights describe the signi cance, and hence the relation, of various features This means that if a feature is more relevant in terms of characterizing cases, it should have more weight (importance) Thus, case feature weighting is crucial in computing similarity, especially weighted distance and weighted feature similarity, which have been introduced in Section 34 The problem of weighting features (ie, assigning weights to features) in a soft computing framework has been investigated by many researchers; see, for example, the recent unsupervised feature selection [47,48] and feature extraction [49] algorithms using a neuro-fuzzy approach [23] and a GA-based approach [50] In this section, some of them are presented.

EXAMPLE: Ned and Nancy Newlywed dream of buying a home with a backyard. They hate paying rent and having nothing but rent receipts to show for it at year end. The tax benef its of homeownership are also a strong enticement to them. The Newlyweds, however, while earning a combined income of $45,000, just haven t been working long enough to have saved up much of a down payment. Their take-home pay is about $31,000 per year, or $2,580 per month. They f igure that they can spend about 40 percent of that, or about $1,000, on mortgage payments. Homes in the area within a reasonable commute to where they both work run about $135,000. Even if

Let us consider a CBR system where a case is usually represented by a feature vector with n components Then the global feature weight refers to a vector w1 ; w2 ; ; wn , where each of its components is a real number in [0,1] It can be interpreted in the way that for the entire case library, different features have different degrees of importance to the solution This is different from the concept of a local feature weight A global feature weight is assigned to each feature of a case, and the same feature for all the cases has the same global weight On the other hand, a local feature weight is assigned to each feature of a speci c case, and a feature may have different local weights for different cases.

In this section, a gradient-based optimization technique and neural networks are used to learn the global feature weights Gradient-based optimization techniques are useful in determining the search directions based on the derivative information of an objective function The optimization is carried out by minimizing a real-valued objective function de ned on an n-dimensional input space In learning the feature weights, an individual weight.

A continuum of positions on rule-following can be pictured. Dogmatically consistent rule-following at one extreme, arbitrary personal choice at the other. Neither of

he may lose that amount If the supplier s share varies significantly from bid to bid, he will never build an infrastructure required to support me in that environment, Doelling reasons Further, price is not the sole factor Sun maintains a scorecard process that measures the total cost of ownership associated with doing business with various suppliers The scorecard ratings do definitely get built back into the auction, he maintains So, we may not necessarily choose the low-cost provider, and we do adjust the shares based on performance Lessons Learned from Dynamic Bidding Experience At the top of the list, as with every new initiative introduction: get strong executive support At Sun, we created dynamic bidding at the urging of our CEO, Doelling notes We needed his strong support because at the outset we did get a lot of pushback internally and from suppliers Start with simple commodities.