Download 91 Common Everyday Items That Bring Huge Profits on Ebay by Scott Foster PDF

By Scott Foster

ISBN-10: 1111111111

ISBN-13: 9781111111113

8-track avid gamers! That's only one of the ninety one goods indexed during this publication so that you can promote for a pleasant revenue on eBay. probably you've gotten a few of these amassing dirt round your home. Many others are available at storage revenues and thrift shops for a fragment of what you may promote them for. So, do you want a bit funds? do you need to begin your personal enterprise promoting stuff on eBay? purchase this publication and start today!

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1 63 Synthetic training data From a collection of documents, one can tease out the semantic relationships among words by generating synthetic queries for a large collection of documents and estimating the wordrelation probabilities from this synthetic data. At a high level, the idea is to take a document and synthesize a query to which the document would be relevant. There are a number of candidate methods for synthesizing a query from a document. One could sample words uniformly at random from the document, but this scheme would generate queries containing a disproportionate number of common words like the, of, and, but.

6 describes a lossy compression technique wherein the matrix requires space only linear in the size of the vocabulary. • Parameter estimation: Given a collection of query, document pairs, use standard machine learning algorithms to estimate optimal parameter values for the parametric model p (q | d)—a model of the likelihood that a sequence of words q is a distillation of (a translation of) a document d. • Search: Given a learned model p (· | d) and a new query q, order documents by relevance to q by ranking them by decreasing p (q | d).

Such a system would be more sophisticated, and hopefully more accurate, than one which adjudicated relevance solely on the basis of word overlap. Loosely speaking, this is the approach described here; this chapter contains algorithms for automatically constructing this database of word relations from a collection of documents. In a sense, weaver has a pedigree in statistical translation, which concerns itself with how to mine large text databases to automatically discover such semantic relations.

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