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We used the process of multiplying the series T = 1+q+2q2 +3q3 +4q4 + by q in order to sum the series because this process will occur again We could also have noted that T = 1/(1 q)2 = (1 q) 2 = 1/p2 by the binomial expansion with

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public void ReadASCIIData( ref byte[] aBuff, ref int aStartPos, out StringBuilder aString) { int dataLength = ReadInt32(ref aBuff, ref aStartPos); aString = new System.Text.StringBuilder(); aString.Length = dataLength; for(int j = 0 ; j < dataLength ; j++) { char myChar = (char)aBuff[aStartPos]; aStartPos++; aString[j] = myChar; } } public static void WriteInt32( { aBuffer.Add((byte) (aValue & aBuffer.Add((byte)((aValue & aBuffer.Add((byte)((aValue & aBuffer.Add((byte)((aValue & } int aValue, ref ArrayList aBuffer ) 0x000000ff)); 0x0000ff00)>>8)); 0x00ff0000)>>16)); 0xff000000)>>24));

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Within the service class the non-functional properties and imported ontologies attributes play a role that is similar to that found in the ontology class with the minor addition of a quality of service nonfunctional property. An extra type of mediator (WW Mediator) is also included, in order to deal with protocol and process-related mismatches between Web services. The nal two attributes de ne the two core WSMO notions for semantically describing Web services: a capability which is a functional description of a Web Service, describing constraints on the input and output of a service through the notions of preconditions, assumptions, postconditions, and effects; and Web service interfaces which specify how the service behaves in order to achieve its functionality. A service

a negative exponent We will generalize this in the next section and see that our last two examples are special cases of what we will call the negative binomial distribution

public static void WriteUint16( ushort aValue, ref ArrayList aBuffer ) { aBuffer.Add((byte) (aValue & 0x00ff)); aBuffer.Add((byte)((aValue & 0xff00)>>8)); } public static void WriteASCIIData( string aString, ref ArrayList aBuff ) { ushort dataLength = (ushort)aString.Length; WriteUint16(dataLength, ref aBuff); for(int j = 0 ; j < dataLength ; j++) { aBuff.Add((byte)aString[j]);

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We now generalize the two special cases we have done and consider waiting for the rth success where r = 1, 2, 3, Again the random variable X denotes the waiting time or the total number of trials necessary to achieve the rth success It is clear that if the xth trial is the rth success, then the previous x 1 trials must contain exactly r 1 successes by a binomial process and in addition the rth success occurs on the xth trial So

We have presented several techniques from Knowledge Discovery that are useful for semi-automatic ontology construction In that light, we propose to decompose the semi-automatic ontology construction process into several phases ranging from domain and data understanding through task de nition via ontology learning to ontology evaluation and re nement A large part of this chapter is dedicated to ontology learning Several scenarios are identi ed in the ontology learning phase depending on different assumptions regarding the provided input data and the expected output: inducing concepts, inducing relations, ontology population, ontology construction, and ontology updating/extension Different groups of Knowledge Discovery techniques are brie y described including unsupervised learning, semi-supervised, supervised and active learning, on-line learning and web-mining, focused crawling, data visualization In addition to providing brief description of these techniques, we also relate them to different ontology learning scenarios that we identi ed.

P(X = x) =

} } public static byte[] AsByteArr( ref ArrayList aBuffer) { int len = aBuffer.Count; byte[] ret = new byte[len]; for(int i = 0 ; i < len ; ++i) { ret[i] = (byte)aBuffer[i]; } return ret; }

Some of the described Knowledge Discovery techniques have already been applied in the context of semi-automatic ontology construction, while others still need to be adapted and tested in that context A challenge for future research is setting up evaluation frameworks for assessing contribution of these techniques to speci c tasks and phases of the ontology construction process In that light, we brie y describe some existing approaches to ontology construction and point to the original papers that provide more information on the approaches, usually including some evaluation of their contribution and performance on the speci c tasks We also related existing work on learning ontologies to different ontology learning scenarios that we have identi ed.

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.NET Barcode Reader Software | Code 128 Scanning DLL Library ...
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