By Dennis Leman, Ad Feelders, Arno Knobbe (auth.), Walter Daelemans, Bart Goethals, Katharina Morik (eds.)
This e-book constitutes the refereed court cases of the joint convention on computer studying and data Discovery in Databases: ECML PKDD 2008, held in Antwerp, Belgium, in September 2008.
The a hundred papers awarded in volumes, including five invited talks, have been conscientiously reviewed and chosen from 521 submissions. as well as the typical papers the amount includes 14 abstracts of papers showing in complete model within the computing device studying magazine and the information Discovery and Databases magazine of Springer.
The convention intends to supply a world discussion board for the dialogue of the newest top of the range study ends up in all parts relating to desktop studying and information discovery in databases. the themes addressed are software of desktop studying and knowledge mining tips on how to real-world difficulties, quite exploratory study that describes novel studying and mining projects and purposes requiring non-standard thoughts.
Read Online or Download Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2008, Antwerp, Belgium, September 15-19, 2008, Proceedings, Part II PDF
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Additional info for Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2008, Antwerp, Belgium, September 15-19, 2008, Proceedings, Part II
The model associates topical and ideological weights to each word in the vocabulary. Topical weights represent how frequently a word is chosen because of a text’s topic independent of an author or speaker’s ideological perspective. Ideological weights, on the other hand, modulate topical weights based on an author or speaker’s ideological perspective. , choosing the word more frequently) we put a larger ideological weight on the word. V2 w1 w2 T V1 w3 Fig. 2. A three-word simplex illustrates how topical weights T are modulated by two diﬀering ideological weights We illustrate the interaction between topical and ideological weights in a three-word simplex in Figure 2.
And Palestinian authors on the Israeli-Palestinian conﬂict. The second corpus, presidential debates, is comprised of spoken words from the Democratic and Republican presidential candidates in 2000 and 2004. org/. ”). The website editors have labeled the ideological perspective of each published article. The bitterlemons corpus has been used to learn individual perspectives , but the previous work was based on naive Bayes models and did not simultaneously model topics and perspectives. The 2000 and 2004 presidential debates corpus consists of the spoken transcripts of six presidential debates and two vice-presidential debates in 2000 and 2004.
This is a standard test in the literature on logistic regression . We refer to this quality measure as ϕsed . DTM Classiﬁer. The second classiﬁer considered is the Decision Table Majority (DTM) classiﬁer [7,6], also known as a simple decision table. The idea behind this classiﬁer is to compute the relative frequencies of the y values for each possible combination of values for x1 , . . , xk . For combinations that do not appear in the dataset, the relative frequency estimates are based on that of the whole dataset.