We introduce a new procedure for pattern recognition, based on the concepts of random projections and nearest neighbors. It can be thought as an improvement of the classical nearest neighbors classification rules. Besides the concept of neighbors we introduce the notion of district, a larger set which will be projected. Then we apply one dimensional k-NN methods to the projected data on randoml...

Over the last decade, distance-based methods have been introduced and then improved in the field of spatial economics to gauge the geographic concentration of activities. There is a growing literature on this theme including new tools, discussions on specific properties and various applications. However no typology of distance-based methods exists. This paper fills this gap. The proposed classi...

We propose a statistical non-parametric classification of brain tissues from an MR image based on the voxel intensities and on the relative anatomical location of the different tissues. Classically, the overlap of the tissue probability distribution functions for voxel intensities can be reduced by using multi-component (T1w,T2w,Pd,...) MR images, but at a much higher cost for image acquisition...

This paper presents the model that can be used to filter the texts which the user is interested in from a large scale of source texts in Chinese or in English. Each text which the user is interested in can be represented as a vector in the vector space of classifiable sememes. The text to be sifted is represented as a vector too. The relevance of the text to the user can be measured by using th...

Label ranking studies the problem of learning a mapping from instances to rankings over a predefined set of labels. We approach this setting from a case-based perspective and propose a sophisticated k-NN framework as an alternative to previous binary decomposition techniques. It exhibits the appealing property of transparency and is based on an aggregation model which allows to incorporate a br...

For the refined IPC classification in the CLEF-IP 2011 task, we constructed classification system with KNN classification which uses PAC (Prior Art Candidate) search results as neighbors. We also slightly modified the neighborhood evaluation. We also furnished a simple PAC search system. We produced some running results both in PAC search and classification, and evaluated our system. Our test s...

An experimental evaluation of Bagging K-nearest neighbor classifiers (KNN) is performed. The goal is to investigate whether varying soft methods of aggregation would yield better results than Sum and Vote. We evaluate the performance of Sum, Product, MProduct, Minimum, Maximum, Median and Vote under varying parameters. The results over different training set sizes show minor improvement due to ...

Our group NEXTI participated in the Query Understanding subtask for Japanese. We extracted subtopic candidates from retrieved web documents. Then, we merged them with query suggestion and query log data. We also identified the vertical intent of each subtopic using a method that combines machine learningand kNN-based methods. We conducted experiments and confirmed the effectiveness of our method.