Download Mastering Information Retrieval and Probabilistic Decision by Daniel Brown (Auth.) PDF
By Daniel Brown (Auth.)
Geared toward details managers in businesses together with local/state govt, libraries and monetary providers. getting to know details Retrieval and Probabilistic selection Intelligence know-how experiences the administration of data and its concentration to humans empowered to make judgements. It presents managers and scholars of knowledge with the assets to appreciate and begin to install info retrieval structures all through their organization and the instruments to reply successfully to the big advancements in new technologies.
- Written via knowledgeable practitioner within the field
- Helps to summarise and clarify the elemental issues
- Covers either merits and demanding situations prone to be encountered in imposing a knowledge Retrieval system
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Additional info for Mastering Information Retrieval and Probabilistic Decision Intelligence Technology
Example text
We see that this encompasses information from straightforward demographic data (such as age and gender) to more subtle information on tastes. Here only tastes in areas of music, food and travel have been included. Clearly, however, the only restriction on the size of this data array is processing power – and as we know through Moore’s law the amount of logic density on silicon chips doubles every 18 months. Entertainment preferences are encoded as well. g. we might interpret this data for a dating agency).
The notable irony of the description above is that we are currently saddled with many, many so-called unique identifiers. The problem is that these identifiers exert influence unidirectionally: they are unique in so far as they apply to the specific organisation with which you are transacting at a given moment. A consequence of this multiplicity of unique identifiers is their very limited scope in acquiring relevant and useful information and the further restriction in making meaningful and important inferences.
If humans had to perform this task over a large number of documents, they would be likely to create some rule of thumb or heuristic in order to speed it up. For example, we might discover that 2,000 words covered approximately four pages and then only perform word counts when we are close to that page count, and have confidence to categorise the document otherwise. Consider the difficulty when we have to decide whether the content of a document is about farming, or more subtly if the document is critical in tone.