The Vision Research Lab at the University of California at Santa Barbara participated in three TRECVID 2007 tasks: rushes summarization, high level feature extraction, and search. This paper describes contributions in the high level feature and search tasks. The high level feature submissions relied on visual features for three runs, audio features exclusively for one, and a fusion of audio and...

A moving kNN query continuously reports the k results (restaurants) nearest to a moving query point (tourist). In addition to the query results, a service provider often returns to mobile client a safe region that bounds the validity of query results in order to minimize the communication cost between the service provider and that mobile client. However, when a service provider is not trustwort...

The k-nearest neighbor (kNN) imputation, as one of the most important research topics in incomplete data discovery, has been developed with great successes on industrial data. However, it is difficult to obtain a mathematical valid and simple procedure to construct confidence intervals for evaluating the imputed data. This chapter studies a new estimation for missing (or incomplete) data that i...

proper forest management needs quantitative and precise estimates of forest stands characteristics. remotely sensed imageries, due to accurate and broad spatial information, has become a cost-effective tool in forest management. classification of forest attributes and generation of thematic maps are among the common applications of remote sensing. the objective of this study was to optimize the...

The KNN algorithm is one of the most popular data mining algorithms. It has been widely and successfully applied to analysis applications across a variety research topics in computer science. This paper illustrates that, despite its success, there remain many challenges classification, including K computation, nearest neighbor selection, search classification rules. Having established these iss...

In multilabel classification each example is represented with features and associated with multiple labels. Multilabel classification aims to predict set of labels for unseen instances. Researchers have developed multilabel classification using both the problem transformation approach and algorithm adaptation approach. An algorithm called ML-kNN that follows algorithm adaptation approach has be...

Efficient search for k nearest neighbors to a given location point (called a KNN query) is an important problem arising in a variety of sensor network applications. In this paper, we investigate in-network query processing strategies under a KNN query processing framework in location-aware wireless sensor networks. A set of algorithms, namely the geo-routing tree, the KNN boundary tree and the ...

Data Mining has great scope in the field of medicine. In this article we introduced one new fuzzy approach for prediction of hepatitis disease. Many researchers have proposed the use of K-nearest neighbor (KNN) for diabetes disease prediction. Some have proposed a different approach by using K-means clustering for reprocessing and then using KNN for classification. In our approach Naive Bayes c...

While k-nearest neighbor queries are becoming increasingly common due to mobile and geospatial applications, orthogonal range queries in high-dimensional data are extremely important in scientific and web-based applications. For efficient querying, data is typically stored in an index optimized for either kNN or range queries. This can be problematic when data is optimized for kNN retrieval and...

This paper describes a machine learning method, called Regression by Feature Projections (RFP), for predicting a real-valued target feature. In RFP training is based on simply storing the projections of the training instances on each feature separately. Prediction is computed through two approximation procedures. The first approximation process is to find the individual predictions of features ...