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In this work we propose an approach based on shape clustering for image retrieval. Firstly, shapes of objects contained into images are represented by means of Fourier descriptors. Then, a fuzzy clustering process is applied to automatically discover a set of shape prototypes representative of a number of semantic categories. The adopted fuzzy clustering algorithm is equipped with a mechanism of partial...
Adaptive software systems are systems that tailor their behavior to each user on the basis of a personalization process. The efficacy of this process is strictly connected with the possibility of an automatic detection of preference profiles, through the analysis of the users' behavior during their interactions with the system. The definition of such profiles should take into account imprecision and...
Recommender systems attempt to predict the needs of Web users and provide them with recommendations to personalize their online experience. In this paper, we propose a neuro-fuzzy approach for the extraction of a recommendation model from usage data encoding user navigational behaviors. Such model is expressed as a set of fuzzy rules which may be exploited to provide personalized link suggestions...
In this paper we present NEWER, a neuro-fuzzy Web recommendation system that dynamically suggests interesting pages to the current user. NEWER employs a neuro-fuzzy approach in order to determine categories of users sharing similar interests and to extract a recommendation model in the form of fuzzy rules expressing associations between user categories and relevances of pages. The derived model is...
User profiling is a fundamental task in Web personalization. Fuzzy clustering is a valid approach to derive user profiles by capturing similar user interests from Web usage data available in log files. Often, fuzzy clustering is based on the assumption that data lay on an Euclidean space; however, clustering based on Euclidean distance can lead the clustering process to find user representations that...
In this paper, we present REXWERE, a software tool designed and implemented in order to extract knowledge from Web usage data in the form of recommendation fuzzy rules useful to provide personalized link suggestions to the visitor of a Web site. REXWERE employs a hybrid approach that combines fuzzy reasoning and neural learning within a working scheme made of several steps. Firstly, a fuzzy clustering...
In this paper we propose a meta-classification framework which is able to represent the accumulated experience from a base-learner in form of knowledge-base and to exploit it whenever a new task has to be tackled. Our meta-dassificr represents a particular meta-learning strategy where a single learning algorithm is employed both at base- and meta-level of learning. The proposed meta-classification...
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