Rule-based specification mining leveraging learning to rank

Leveraging Unlabeled Data for Crowd Counting by Learning to Rank

We propose a novel crowd counting approach that leverages abundantly available unlabeled crowd imagery in a learning-to-rank framework. To induce a ranking of cropped images , we use the observation that any sub-image of a crowded scene image is guaranteed to contain the same number or fewer persons than the super-image. This allows us to address the problem of limited size of existing datasets...

Effective Learning to Rank Persian Web Content

Persian language is one of the most widely used languages in the Web environment. Hence, the Persian Web includes invaluable information that is required to be retrieved effectively. Similar to other languages, ranking algorithms for the Persian Web content, deal with different challenges, such as applicability issues in real-world situations as well as the lack of user modeling. CF-Rank, as a ...

Data sanitization in association rule mining based on impact factor

Data sanitization is a process that is used to promote the sharing of transactional databases among organizations and businesses, it alleviates concerns for individuals and organizations regarding the disclosure of sensitive patterns. It transforms the source database into a released database so that counterparts cannot discover the sensitive patterns and so data confidentiality is preserved ag...

Sampling based multi-agent joint learning for association rule mining

In order to achieve distributed data mining quickly and efficiently, this paper proposes SMAJL, a model for sampling based multi-agent joint learning which integrates sampling technology and multi-agent argumentation in the field of association rule mining. By sampling, this model can reduce the size of dataset and improve the efficiency of data mining; through joint learning from argumentation...

Constraint-Based Association Rule Mining