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pro vyhledávání: '"Ranking SVM"'
Akademický článek
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Publikováno v:
ACM transactions on intelligent systems and technology
7 (2015): 8–35. doi:10.1145/2766459
info:cnr-pdr/source/autori:Muntean C.I.; Nardini F.M.; Silvestri F.; Baraglia R./titolo:On learning prediction models for tourists paths/doi:10.1145%2F2766459/rivista:ACM transactions on intelligent systems and technology (Print)/anno:2015/pagina_da:8/pagina_a:35/intervallo_pagine:8–35/volume:7
7 (2015): 8–35. doi:10.1145/2766459
info:cnr-pdr/source/autori:Muntean C.I.; Nardini F.M.; Silvestri F.; Baraglia R./titolo:On learning prediction models for tourists paths/doi:10.1145%2F2766459/rivista:ACM transactions on intelligent systems and technology (Print)/anno:2015/pagina_da:8/pagina_a:35/intervallo_pagine:8–35/volume:7
In this article, we tackle the problem of predicting the “next” geographical position of a tourist, given her history (i.e., the prediction is done accordingly to the tourist’s current trail) by means of supervised learning techniques, namely G
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4888e150ff6c37950f10f3596b063bab
https://zenodo.org/record/8128600
https://zenodo.org/record/8128600
Akademický článek
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Publikováno v:
Applied Intelligence. 52:2465-2479
Positive and unlabeled learning (PU learning) has been studied to address the situation in which only positive and unlabeled examples are available. Most of the previous work has been devoted to identifying negative examples from the unlabeled data,
Autor:
Jacky Keung, Kwabena E. Bennin, Xiao Yu, Zhou Xu, Jin Liu, Qing Li, Junping Wang, Xiaohui Cui
Publikováno v:
IEEE Transactions on Reliability. 69:139-153
Context: Ranking-oriented defect prediction (RODP) ranks software modules to allocate limited testing resources to each module according to the predicted number of defects. Most RODP methods overlook that ranking a module with more defects incorrectl
Autor:
Rasmita Dash
Publikováno v:
Journal of King Saud University: Computer and Information Sciences, Vol 32, Iss 2, Pp 232-247 (2020)
High dimensional search space in microarray data with large number of genes and few dozen of samples increases the complexity of analysis of such databases. All the genes are not significant and hence informative genes are required to be extracted. S
Publikováno v:
IEEE Access, Vol 8, Pp 140261-140272 (2020)
In the last 70 years, the automatic text summarization work has become more and more important because the amount of data on the Internet is increasing so fast, and automatic text summarization work can extract useful information and knowledge what u
Autor:
Shengkang Yu, Xueyi Zhao, Fei Wu, Xi Li, Xuelong Li, Jingdong Wang, Yueting Zhuang, Zhongfei Zhang
Publikováno v:
IEEE Transactions on Big Data. 5:588-600
As an important and challenging problem, knowledge representation and inference are typically carried out in a knowledge embedding framework over a multi-relational knowledge graph, and thus have a wide range of applications such as semantic retrieva
Autor:
Faisal Javed, Maqsood Hayat
Publikováno v:
Genomics. 111:1325-1332
The emergence of numerous genome projects has made the experimental classification of the protein localization almost impossible due to the exponential increase in the number of protein samples. However, most of the applications are merely developed
Publikováno v:
IEEE Transactions on Knowledge and Data Engineering. 31:1181-1193
Ranking SVM, which formalizes the problem of learning a ranking model as that of learning a binary SVM on preference pairs of documents, is a state-of-the-art ranking model in information retrieval. The dual form solution of a linear Ranking SVM mode