By Atefeh Farzindar, Vlado Keselj
This publication constitutes the refereed complaints of the twenty third convention on man made Intelligence, Canadian AI 2010, held in Ottawa, Canada, in May/June 2010. The 22 revised complete papers offered including 26 revised brief papers, 12 papers from the graduate scholar symposium and the abstracts of three keynote shows have been rigorously reviewed and chosen from ninety submissions. The papers are prepared in topical sections on textual content type; textual content summarization and IR; reasoning and e-commerce; probabilistic computer studying; neural networks and swarm optimization; laptop studying and information mining; ordinary language processing; textual content analytics; reasoning and making plans; e-commerce; semantic internet; desktop studying; and information mining.
Read Online or Download Advances in Artificial Intelligence: 23rd Canadian Conference on Artificial Intelligence, Canadian AI 2010, Ottawa, Canada, May 31 - June 2, 2010, PDF
Best data mining books
Information Mining, the automated extraction of implicit and in all likelihood important details from information, is more and more utilized in advertisement, clinical and different software areas.
Principles of information Mining explains and explores the critical strategies of knowledge Mining: for type, organization rule mining and clustering. each one subject is obviously defined and illustrated through precise labored examples, with a spotlight on algorithms instead of mathematical formalism. it really is written for readers and not using a powerful heritage in arithmetic or information, and any formulae used are defined in detail.
This moment version has been accelerated to incorporate extra chapters on utilizing common trend timber for organization Rule Mining, evaluating classifiers, ensemble class and working with very huge volumes of data.
Principles of information Mining goals to assist basic readers boost the mandatory figuring out of what's contained in the 'black box' to allow them to use advertisement information mining applications discriminatingly, in addition to allowing complicated readers or educational researchers to appreciate or give a contribution to destiny technical advances within the field.
Suitable as a textbook to aid classes at undergraduate or postgraduate degrees in a variety of topics together with computing device technology, company experiences, advertising, man made Intelligence, Bioinformatics and Forensic technological know-how.
Steve Lohr, a expertise reporter for the hot York instances, chronicles the increase of massive information, addressing state-of-the-art enterprise options and analyzing the darkish facet of a data-driven global. Coal, iron ore, and oil have been the major effective resources that fueled the commercial Revolution. at the present time, info is the important uncooked fabric of the knowledge financial system.
Additional info for Advances in Artificial Intelligence: 23rd Canadian Conference on Artificial Intelligence, Canadian AI 2010, Ottawa, Canada, May 31 - June 2, 2010,
Naive Bayesian results with λ = 1 Classiﬁed legitimate Classiﬁed spam Total 5 Actually legitimate Actually spam 476 32 23 236 499 268 Total 508 259 767 Experimental Results and Evaluations Our experiments were performed on a spambase data set from UCI Machine Learning Repository . The data set consists of 4601 instances, with 1813 instances as spam, and 2788 instances as legitimate, each instance is described by 58 attributes. Our goal is to compare our approach with the original naive Bayesian spam ﬁlter in terms of the error rate that a legitimate email is classiﬁed as spam, the precision and recall for both legitimate and spam emails, and the cost-sensitive measure suggested by Androutsopoulos et al.
PN − λNN ) + (λNP − λPP ) α= (15) The decision rules (P)-(N) can be expressed concisely as: (P) If P r(C|x) ≥ α and P r(C|x) ≥ γ, decide x ∈ POS(C); (B) If P r(C|x) ≤ α and P r(C|x) ≥ β, decide x ∈ BND(C); (N) If P r(C|x) ≤ β and P r(C|x) ≤ γ, decide x ∈ NEG(C). Each rule is deﬁned by two out of the three parameters. The conditions of rule (B) suggest that α > β may be a reasonable constraint; it will ensure a well-deﬁned boundary region. By setting α > β, namely, (λPN − λBN ) (λBN − λNN ) > , (λPN − λBN ) + (λBP − λPP ) (λBN − λNN ) + (λNP − λBP ) (16) we obtain the following condition on the loss function : (c1).
1]. We split the spambase data set into a training set of 3834 instances, and a testing set of 767 instances. Since the attributes in the input data set have continuous values, entropy-MDL  is used as the discretization method applied to both the training and testing data sets before the calculations of probabilities. For the cost-sensitive evaluations, we assume that misclassifying a legitimate email as spam is λ times more costly than misclassifying a spam email as legitimate. We considered three diﬀerent λ values (λ = 9, λ = 3, and λ = 1) for the original naive Bayesian spam ﬁlter.
Advances in Artificial Intelligence: 23rd Canadian Conference on Artificial Intelligence, Canadian AI 2010, Ottawa, Canada, May 31 - June 2, 2010, by Atefeh Farzindar, Vlado Keselj