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1 Summary of notations Description Average media bit-rate Media block size Length of a transmission cycle Number of disks Disk storage capacity Number of recording surfaces in a disk Number of tracks per recording surface in a disk Size of a disk sector Number of zones in a disk Number of sectors per track in zone i Minimum number of sectors per track Maximum number of sectors per track Transfer rate in zone i Minimum transfer rate Disk platter rotation rate Fixed overhead in disk read Seek time in disk read Rotational latency in disk read Transfer time in disk read Disk seek function Maximum number of requests that can be served in a service round Head-switching time in disk Total track-to-track seek time in serving a request Earliest completion time for retrieving track i Latest completion time for retrieving track i Minimum time for retrieving one track Deviation bound for track-retrieval completion times Server load/utilization Number of active streams for a server Server utilization Number of rebuild blocks retrieved by a working disk in a round Rate at which rebuild data are retrieved from the working disks Rate at which lost data are rebuilt Buffer requirement for playback process Buffer requirement for rebuild process Total buffer requirement without buffer sharing Total buffer requirement with buffer sharing Symbol Rv Q Tr ND G Nsuf Ntrk S Nzone Yi Ymin Ymax Xi X min W tseek trot txfr f seek (n) K thsw ttrack ei li Dasyn u U nb Rrb Rrebuild Bp Br Bsum Bshare Notes Parameter in bytes per second (e.



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g, 150,000 Bps) Parameter in bytes (eg.

Supervised learning for text document categorization can be applied when a set of prede ned topic categories, such as arts, education, science, are provided as well as a set of documents labeled with those categories The task is to classify new (previously unseen) documents by assigning each document one or more content categories (eg, ontology concepts or relations) This is usually performed by representing documents as word-vectors and using documents that have already been assigned to the categories, to generate a model for assigning content categories to new documents (Jackson and Moulinier, 2002; Sebastiani, 2002) In the word-vector representation of a document, a vector of word frequencies is formed taking all the words occurring in all the documents (usually several thousands of words) and often applying some feature subset selection approach (Mladenic and Grobelnik, 2003).





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65,336 bytes) Computed from Q/R, in seconds Parameter Parameter in bytes Parameter in numbers Parameter in numbers Parameter in bytes Parameter in numbers Parameter in numbers Parameter in numbers Parameter in numbers Computed, in bytes per second Computed, in bytes per second Parameter in cycles per second Parameter in seconds Random variable in seconds Random variable in seconds Random variable in seconds Seek time for a seek distance of n tracks Computed, in number of requests Parameter in seconds Random variable, in seconds Random variable, in seconds Random variable, in seconds Parameter in seconds Parameter in seconds Variable, in number of streams Variable, in number of streams Variable, real number ranging from 0 to 1 Computed, in number of media blocks Computed, in bytes per second Computed, in bytes per second Computed, in bytes Computed, in bytes Computed, in bytes Computed, in bytes.

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make sure that my wife had signed off on the trades that I took and the risk that I incurred, he kept telling me that he was no longer worried about what kind of trader I would become I could sense that he had less and less that he wanted to teach me, and more and more that he wanted me to teach him about what I was doing He didn t need the information because he was going to trade like me; rather, he wanted me to teach him what I was doing so that I could prove that I was focused And I was definitely focused! I only traded support and resistance from the daily and four-hour charts That doesn t include the retail carry trade, which I opened the very next week with $20,000, and which I talk more about in Appendix A.

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The representation of a particular document contains many zeros, as most of the words from the collection do not occur in a particular document The categories can be organized into a topic ontology, for example, the MeSH ontology for medical subject headings or the Yahoo! hierarchy of Web documents that can be seen as a topic ontology1 Different Knowledge Discovery methods have been applied and evaluated on different document categorization problems For instance, on the taxonomy of US patents, on Web documents organized in the Yahoo! Web directory (McCallum et al, 1998; Mladenic, 1998; Mladenic and Grobelnik 2004), on the DMoz Web directory (Grobelnik and Mladenic 2005), on categorization of Reuters news articles (Koller and Sahami, 1997, Mladenic et al, 2004).

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Documents can also be related in ways other than common words (for instance, hyperlinks connecting Web documents) and these connections can be also used in document categorization (eg, Craven and Slattery, 2001)..

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