For consecutive model-based control design, approximate identication of linear models should be performed on the basis of a feedback-relevant criterion, compatible with the control design. For an H1-norm based control design, a procedure is presented to estimate a possibly unstable and feedback controlled plant by using an H1-norm based feedback-relevant identi cation criterion. It is shown tha...

In this paper we propose to incorporate a feedback loop, into the ordinal correlation framework and apply it to shapebased image retrieval. The user’s feedback on the relevance of the retrieval results is used to tune the weights of the similarity measure. Statistics from the features of both relevant and irrelevant items are used to estimate the weights. Moreover, the information accumulated f...

Pseudo relevance feedback is empirically known as a useful method for enhancing retrieval performance. For example, we can apply the Rocchio method, which is well-known relevance feedback method, to the results of an initial search by assuming that the top-ranked documents are relevant a priori. In this paper, for searching NTCIR-3 patent test collection through pseudo feedback, we try to emplo...

We investigate models for content-based image retrieval with relevance feedback, in particular focusing on the exploration-exploitation dilemma. We propose quantitative models for the user behavior and investigate implications of these models. Three search algorithms for efficient searches based on the user models are proposed and evaluated. In the first model a user queries a database for the ...

Relevance feedback can effectively improve the performance of content-based multimedia retrieval systems. To be effective, a relevance feedback approach must be able to efficiently capture the user’s query concept from a very limited number of training samples. To address this issue, we propose a novel adaptive classification method using random forests, which is a machine learning algorithm wi...

Abstract This study investigated whether positive feedback from same-age peers can modify self-relevant cognitive processes of high socially anxious youth in a direction. Thirty-three and 32 non-socially undergraduate students (17–22 years) gave an impromptu speech received either or neutral post-speech. Anticipatory processing (AP) was rated prior to the via self-report. One week later partici...

This paper details our experiments carried out at TREC 2008 Relevance Feedback Track. We focused on the analysis of feedback documents, both relevant and non-relevant, to explore more useful information to improve retrieval performance. In our experiments, local co-occurrence model and a Rocchio formula were used to select good expansion terms. Five runs were submitted. These runs used differen...

In this paper we investigate the effectiveness of Relevance Feedback algorithms inspired by Quantum Detection in the context of the Dynamic Domain track. Documents and queries are represented as vectors; the query vector is projected into the subspace spanned by the eigenvector which maximizes the distance between the distribution of quantum probability of relevance and the distribution of quan...