Atefeh Farzindar, Vlado Keselj's Advances in Artificial Intelligence: 23rd Canadian PDF

By Atefeh Farzindar, Vlado Keselj

ISBN-10: 3642130585

ISBN-13: 9783642130588

This booklet constitutes the refereed lawsuits of the twenty third convention on synthetic Intelligence, Canadian AI 2010, held in Ottawa, Canada, in May/June 2010. The 22 revised complete papers awarded including 26 revised brief papers, 12 papers from the graduate pupil symposium and the abstracts of three keynote shows have been rigorously reviewed and chosen from ninety submissions. The papers are geared up in topical sections on textual content type; textual content summarization and IR; reasoning and e-commerce; probabilistic desktop studying; neural networks and swarm optimization; computing device studying and information mining; normal language processing; textual content analytics; reasoning and making plans; e-commerce; semantic net; laptop studying; and knowledge mining.

Show description

Read 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

Download e-book for iPad: Post-mining of Association Rules: Techniques for Effective by Yanchang Zhao

There's usually numerous organization principles chanced on in info mining perform, making it tricky for clients to spot those who are of specific curiosity to them. accordingly, it is very important eliminate insignificant principles and prune redundancy in addition to summarize, visualize, and post-mine the came across principles.

Data Preparation for Data Mining Using SAS (The Morgan by Mamdouh Refaat PDF

Are you an information mining analyst, who spends as much as eighty% of some time assuring facts caliber, then getting ready that information for constructing and deploying predictive versions? And do you discover plenty of literature on info mining conception and ideas, but if it involves useful recommendation on constructing stable mining perspectives locate little “how to” details?

Mining eBay Web Services: Building Applications with the - download pdf or read online

More suitable velocity, Accuracy, and Convenience—Yours for the TakingeBay is regularly enhancing the beneficial properties it deals purchasers and dealers. Now, the most important advancements are ones you could construct for your self. Mining eBay net companies teaches you to create customized purposes that automate trading projects and make searches extra targeted.

Statistics for Big Data For Dummies - download pdf or read online

The short and simple method to make experience of data for large information Does the topic of knowledge research make you dizzy? you have got come to the fitting position! data for large info For Dummies breaks this often-overwhelming topic down into simply digestible elements, delivering new and aspiring info analysts the root they should prevail within the box.

Extra info for Advances in Artificial Intelligence: 23rd Canadian Conference on Artificial Intelligence, Canadian AI 2010, Ottawa, Canada, May 31 - June 2, 2010,

Sample text

The overall risk R is the expected loss associated with a given decision rule. Since R(τ (x)|x) is the conditional risk associated with action τ (x), the overall risk is defined by: R= R(τ (x)|x)P r(x), (7) x where the summation is over the set of all possible descriptions of emails. If τ (x) is chosen so that R(τ (x)|x) is as small as possible for every x, the overall risk R is minimized. Thus, the optimal Bayesian decision procedure can be formally stated as follows. For every x, compute the conditional risk R(ai |x) for i = 1, .

Yao, and J. Luo The Naive Bayesian Spam Filtering The naive Bayesian spam filtering is a probabilistic classification technique of email filtering [14]. It is based on Bayes’ theorem with naive (strong) independence assumptions [6,11,14]. , xn are the values of attributes of emails. Let C denote the legitimate class, and C c denote the spam class. Based on Bayes’ theorem and the theorem of total probability, given the vector of an email, the conditional probability that this email is in the legitimate class is: P r(C|x) = P r(C)P r(x|C) , P r(x) (1) where P r(x) = P r(x|C)P r(C) + P r(x|C c )P r(C c ).

Another facet of the evaluation concerns the relation between polarity and emotions. We apply a novel approach which arranges neutrality, polarity and emotions hierarchically. This method significantly outperforms the corresponding “flat” approach which does not take into account the hierarchical information. We also compare corpus-based and lexical-based feature sets and we choose the most appropriate set of features to be used in our hierarchical classification experiments. Keywords: Sentiment analysis, emotion in text, emotion recognition, text classification, hierarchical classification.

Download PDF sample

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

by Jeff

Rated 4.42 of 5 – based on 22 votes