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| 1 | PSVM: a preference-enhanced SVM model using preference data for classification显示文摘Classification is an essential task in data mining, machine learning and pattern recognition areas.Conventional classification models focus on distinctive samples from different categories. There are fine-grained differences between data instances within a particular category. These differences form the preference information that is essential for human learning, and, in our view, could also be helpful for classification models. In this paper, we propose a preference-enhanced support vector machine(PSVM), that incorporates preference-pair data as a specific type of supplementary information into SVM. Additionally, we propose a two-layer heuristic sampling method to obtain effective preference-pairs, and an extended sequential minimal optimization(SMO)algorithm to fit PSVM. To evaluate our model, we use the task of knowledge base acceleration-cumulative citation recommendation(KBA-CCR) on the TREC-KBA-2012 dataset and seven other datasets from UCI,Stat Lib and mldata.org. The experimental results show that our proposed PSVM exhibits high performance with official evaluation metrics. | Lerong MA Dandan SONG Lejian LIAO Jingang WANG | 2017 | Science China(Information Sciences)2017，60，12： | 3 |
| 2 | A Latent Entity-Document Class Mixture of Experts Model for Cumulative Citation Recommendation显示文摘Knowledge Bases(KBs) are valuable resources of human knowledge which contribute to many applications. However, since they are manually maintained, there is a big lag between their contents and the upto-date information of entities. Considering a target entity in KBs, this paper investigates how Cumulative Citation Recommendation(CCR) can be used to effectively detect its worthy-citation documents in large volumes of stream data. Most global relevant models only consider semantic and temporal features of entity-document instances,which does not sufficiently exploit prior knowledge underlying entity-document instances. To tackle this problem,we present a Mixture of Experts(ME) model by introducing a latent layer to capture relationships between the entity-document instances and their latent class information. An extensive set of experiments was conducted on TREC-KBA-2013 dataset. The results show that the model can significantly achieve a better performance gain compared to state-of-the-art models in CCR. | Lerong Ma Lejian Liao DANDan Song Jingang Wang | 2018 | Tsinghua Science and Technology2018，23，6： | 2 |
| 3 | Entity Burst Discriminative Model for Cumulative Citation Recommendation显示文摘Knowledge base acceleration-cumulative citation recommendation(KBA-CCR)aims to detect citation-worthiness documents from a chronological stream corpus for a set of target entities in a knowledge base.Most previous works only consider a number of semantic features between documents and target entities in the knowledge base,and then use powerful machine learning approaches such as logistic regression to classify relevant documents and non-relevant documents.However,the burst activities of an entity have been proved to be a significant signal to predict potential citations.In this paper,an entity burst discriminative model(EBDM)is presented to substantially exploit such burst features.The EBDM presents a new temporal representation based on the burst features,which can capture both temporal and semantic correlations between entities and documents.Meanwhile,in contrast to the bag-of-words model,the EBDM can significantly decrease the number of non-zero entries of feature vectors.An extensive set of experiments were conducted on the TREC-KBA-2012 dataset.The results show that the EBDM outperforms the performance of the state-of-the-art models. | Lerong Ma | 2019 | Journal of Beijing Institute of Technology2019，28，2： | 0 |
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