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[Clustering] [Order] [Other]
Supervised Clustering is a hybrid task combining features of two common grouping tasks: supervised classification and and clustering. In supervised clustering, each training example is a partition of objects.The task is then to learn from a training set, a rule for partitioning unseen object sets. A general method for learning such partitioning rules is useful in any situation where explicit algorithms for deriving partitions are hard to formalize, while individual examples of correct partitions are easy to specify. In the past, clustering techniques have been applied to such problems, despite being essentially unsuited to the task.
Related Publications [ 031 ]
The term order as a sequence of objects that are sorted according to some property, such as size, preference, or price. Lists of ordered objects are widely used as representational forms. Such ordered objects include Web search results or best seller lists. The analysis of orders is useful for sensory tests, information retrieval, or decision making.
Object Ranking is a task to learn a function for ranking objects from given sample orders. Training sample orders are sorted according to the degree of the target preference to learn. Objects in these orders are represented by feature vectors. From these samples, an object ranking method acquires a ranking function. By applying this learned function, unordered objects can be sorted according to the degree of the target preference. Note that objects that don’t appeared in training samples have to be ordered by referring feature vectors of objects.
A Survey on Object Ranking @ PL-09 Workshop
Related Publications [ 082, 070, 057, 048, 033 ]
A method of using clustering techniques to partition a set of orders. k-o'means algorithm is a modified version of a k-means method, adjusted to handle orders.
Related Publications [ 091, 069, 042 ]
A recommender system suggests the items expected to be preferred by the users. Recommender systems use collaborative filtering to recommend items by summarizing the preferences of people who have tendencies similar to the user preference (see right Figure). Traditional collaborative filtering algorithms adopted a scoring method (Figure (a)), in which preferences are measured using an n-point-scale, or a rating method, which employs scales such as good-fair-poor or gold-silver-bronze. We propose some collaborative filtering algorithms adopting a ranking method (Figure (b)). In this ranking method, the preferences are represented by orders, which are sorted item sequences according to the users' preferences.
| (a) Scoring Method | (b) Ranking Method |
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Related Publications [ 064, 049, 039 ]
An experimental multimodal disputation system, Mr.Bengo, is a knowledge based system with multimodal user interfaces composed of modules for face recognition, face animation, speech recognition, speech synthesis, and text interface. In a virtual court, an attorney agent interact with users and disputes with a prosecutor agent.
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"Mr.Bengo" Homepage (in Japanese) Related Publications [ 013, Demo Video ] |
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