توجه: محتویات این صفحه به صورت خودکار پردازش شده و مقاله‌های نویسندگانی با تشابه اسمی، همگی در بخش یکسان نمایش داده می‌شوند.
۱A New Approach for Ranking Web Pages
نویسنده(ها): ،
اطلاعات انتشار: نهمین کنفرانس سالانه انجمن کامپیوتر ایران، سال
تعداد صفحات: ۱۰
Today , information retrieval is a common for internet users. The huge size and heterogeneous structure of the web has caused new problems for its users. In this immense resource of information , it is exceedingly difficult for to locate resources that are both high in quality and relevant to their information needs. One of the main tasks of each search is providing users with these high quality and relevant resources. Search engines use ranking algorithm in order to sort the documents related to the user need.
For ranking web resource , several ranking methods have been introduced. In this paper, we present the most famous web ranking algorithm for ranking web page, which exploits hyperlink structure as well as document contents for ranking resources , with the intent of overcoming the draw backs of current ranking algorithms.
<\div>

۲Improving the Classification of Unknown Documents by Concept Graph
نویسنده(ها): ، ،
اطلاعات انتشار: چهاردهمین کنفرانس بین المللی سالانه انجمن کامپیوتر ایران، سال
تعداد صفحات: ۶
Concept graph is a graph that represents the relationships between language concepts. In this structure the relationship between any two words is demonstrated by a weighted edge such that the value of this weight is interpreted as the degree of the relevance of two words. Having this graph, we can obtain most relevant words to a special term. In this paper, we propose a method for improving the classification of documents from unknown sources by means of concept graph. In our method, initially some features are selected from a training set by a well–known feature selection algorithm. Then, by extracting most relevant words for each class from the concept graph, a more effective feature set is produced. Our experimental results identify an improvement of 1% and 8% in precision and recall measures, respectively.<\div>

۳Quantitative Similarity–based Evaluation of Text Retrieval Algorithms
نویسنده(ها): ، ،
اطلاعات انتشار: چهاردهمین کنفرانس بین المللی سالانه انجمن کامپیوتر ایران، سال
تعداد صفحات: ۶
Text retrieval engines, such as search engines,always return a list of documents in response to a given query. Existing evaluations of text retrieval algorithms mostly use Precision and Recall of the returned list of documents as main quality measures of a search engine. In this paper, we propose a novel approach for comparing different algorithms adopted by different search engines and evaluate their performance. In our approach, the results of each algorithm is treated as an inter–related set of documents and the effectiveness of the algorithm is evaluated based on the degree of relation in the set of documents. After verifying the correctness of the evaluation measure by examining the results of the two retrieval algorithms, BM25 and pivoted normalization, and comparing these results with an ideal ranking, we compare the results of these algorithms and investigate the impact of certain major factors like stemming on the results of the suggested algorithm. The effectiveness of our proposed method is justified through obtained xperimental results<\div>

۴Content–Based Concept Drift Detection for Email Spam Filtering
اطلاعات انتشار: International Journal Information and Communication Technology Research، دوم،شماره۳، Nov ۲۰۱۰، سال
تعداد صفحات: ۷
The continued growth of Email usage, which is naturally followed by an increase in unsolicited emails so called spams, motivates research in spam filtering area. In the context of spam filtering systems, addressing the evolving nature of spams, which leads to obsolete the related models, has been always a challenge. In this paper an adaptive spam filtering system based on language model is proposed which can detect concept drift based on computing the deviation in email contents distribution. The proposed method can be used along with any existing classifier; particularly in this paper we use Naïve Bayes method as classifier. The proposed method has been evaluated with Enron data set. The results indicate the efficiency of the method in detecting concept drift and its superiority over Naïve Bayes classifier in terms of accuracy.

۵Learning to Exploit Different Translation Resources for Cross Language Information Retrieval
نویسنده(ها): ، ،
اطلاعات انتشار: International Journal Information and Communication Technology Research، ششم،شماره۱، winter ۲۰۱۴، سال
تعداد صفحات: ۱۴

۶Analyzing Content–based Heuristics for Persian Web Spam Detection
نویسنده(ها): ،
اطلاعات انتشار: International Journal Information and Communication Technology Research، ششم،شماره۳، Summer ۲۰۱۴، سال
تعداد صفحات: ۱۵
نمایش نتایج ۱ تا ۶ از میان ۶ نتیجه

ارتباط با ما

لینک‌های مفید