Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In unsupervised learning, where no training takes place, one simply hopes that the unsupervised learner will work ll on any unlabeled test collection. Hover, when the variability in the data is large, such hope may be unrealistic; a tuning of the unsupervised algorithm may then be necessary in order to perform ll on new test collections. ,  show how to perform such a tuning in the context of unsupervised document clustering, by (i) introducing a degree of freedom, , into two leading informationtheoretic clustering algorithms, through the use of generalized mutual information quantities; and (ii) selecting the value of based on clusterings of similar, but supervised document collections (crossinstance tuning). One option is to perform a tuning that directly minimizes the error on the supervised data sets; another option is to use “strapping” (Eisner and Karakos, 2005), which builds a classifier that learns to distinguish good from bad clusterings, and then selects the with the best predicted clustering on the test set. Experiments from the “20 Newsgroups” corpus show that, although both techniques improve the performance of the baseline algorithms, “strapping” is clearly a better choice for cross-instance tuning.
['Damianos Karakos', 'Jason Eisner', 'Sanjeev Khudanpur', 'Carey E. Priebe']
I am writing a research paper on a idea Cross-Instance Tuning of Unsupervised Document Clustering Algorithms
[{"from": "human", "value": "I am writing a research paper on a idea Cross-Instance Tuning of Unsupervised Document Clustering Algorithms"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In unsupervised learning, where no training takes place, one simply hopes that the unsupervised learner will work ll on any unlabeled test collection. Hover, when the variability in the data is large, such hope may be unrealistic; a tuning of the unsupervised algorithm may then be necessary in order to perform ll on new test collections. ,  show how to perform such a tuning in the context of unsupervised document clustering, by (i) introducing a degree of freedom, , into two leading informationtheoretic clustering algorithms, through the use of generalized mutual information quantities; and (ii) selecting the value of based on clusterings of similar, but supervised document collections (crossinstance tuning). One option is to perform a tuning that directly minimizes the error on the supervised data sets; another option is to use \u201cstrapping\u201d (Eisner and Karakos, 2005), which builds a classifier that learns to distinguish good from bad clusterings, and then selects the with the best predicted clustering on the test set. Experiments from the \u201c20 Newsgroups\u201d corpus show that, although both techniques improve the performance of the baseline algorithms, \u201cstrapping\u201d is clearly a better choice for cross-instance tuning."}]