Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  address the problem of one-class classification. Taking into account the fact that in some applications, the given training samples are rather limited,  attempt to utilize the advantages of Multi-task Learning (MTL), where the data of related tasks may share similar structure and helpful information.  then propose an MTL framework for one-class classification. The framework derives from the one-class v-SVM and makes use of related tasks by constraining them to have similar solutions. This formulation can be cast into a second-order cone program, which achieves a global solution and is solved efficiently. Further, the framework also maintains the favorable property of the v parameter in the v-SVM, which can control the fraction of outliers and support vectors, in one-class classification. This framework also connects with several existing models. Experimental results on both synthetic and real-world datasets demonstrate the properties and advantages of our proposed model.
['Haiqin Yang', 'Irwin King', 'Michael R. Lyu']
I am writing a research paper on a idea Multi-task Learning for one-class classification
[{"from": "human", "value": "I am writing a research paper on a idea Multi-task Learning for one-class classification"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  address the problem of one-class classification. Taking into account the fact that in some applications, the given training samples are rather limited,  attempt to utilize the advantages of Multi-task Learning (MTL), where the data of related tasks may share similar structure and helpful information.  then propose an MTL framework for one-class classification. The framework derives from the one-class v-SVM and makes use of related tasks by constraining them to have similar solutions. This formulation can be cast into a second-order cone program, which achieves a global solution and is solved efficiently. Further, the framework also maintains the favorable property of the v parameter in the v-SVM, which can control the fraction of outliers and support vectors, in one-class classification. This framework also connects with several existing models. Experimental results on both synthetic and real-world datasets demonstrate the properties and advantages of our proposed model."}]