Searching for authors named Dimitrios Gunopulos – sorted by Relevance.
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Efficient Local Flexible Nearest Neighbor Classification
- The nearest neighbor technique is a simple and appealing method to address classification problems. It relies on the assumption of locally constant class conditional probabilities. This assumption becomes invalid in high dimensions with a finite number of examples due to the curse of dimensionality.
- Cited by 2 (0 self) – Add To MetaCart
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An Efficient Approach for Approximating Multi-dimensional Range Queries and Nearest Neighbor Classification in Large Datasets
- We propose a locally adaptive technique to address the problem of setting the bandwidth parameters optimally for kernel density estimation. Our technique is efficient and can be performed in only two dataset passes. We also show how to apply our technique to efficiently solve range query appro
- Cited by 2 (1 self) – Add To MetaCart
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Exploiting Locality for Scalable Information Retrieval in Peer-to-Peer Networks
- An important problem in unstructured peer-to-peer (P2P) networks is the efficient content-based retrieval of documents shared by other peers. However, existing searching mechanisms are not scaling well because they are either based on the idea of flooding the network with queries or because they req
- Cited by 13 (3 self) – Add To MetaCart
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Incremental Support Vector Machine Construction
- SVMs suffer from the problem of large memory requirement and CPU time when trained in batch mode on large data sets. We overcome these limitations, and at the same time make SVMs suitable for learning with data streams, by constructing incremental learning algorithms.
- Cited by 10 (0 self) – Add To MetaCart
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Adaptive Nearest Neighbor Classification using Support Vector Machines
- The nearest neighbor technique is a simple and appealing method to address classification problems. It relies on the assumption of locally constant class conditional probabilities. This assumption becomes invalid in high dimensions with a finite number of examples due to the curse of dimensionality
- Cited by 21 (0 self) – Add To MetaCart
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Concept Learning with geometric hypotheses
- We present a general approach to solving the minimizing disagreement problem for geometric hypotheses with finite VC-dimension. These results also imply efficient agnostic-PAC learning of these hypotheses classes. In particular we give an O(n min(ff+1=2;2k01) log n) algorithm that solves th
- Cited by 8 (0 self) – Add To MetaCart
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Online Information Compression in Sensor Networks
- Abstract-In the emerging area of wireless sensor networks, one of the most typical challenges is to retrieve historical information from the sensor nodes. Due to the resource limitation of sensor nodes (processing, memory, bandwidth, and energy), the collected information of sensor nodes has to be c
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Clustering Gene Expression Data in SQL Using Locally Adaptive Metrics
- groups of data according to a certain similarity measure. Clustering suers from the curse of dimensionality. It is not meaningful to look for clusters in high dimensional spaces as the average density of points anywhere in input space is likely to be low. As a consequence, distance functions that eq
- Cited by 1 (0 self) – Add To MetaCart
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Handling Multimedia Objects in Peer-to-Peer Networks
- Video-on-demand systems and services [4, 2] are predominantly offered over dedicated private networks with the help of large servers [3, 13]. Such systems are restricted by the number of concurrent accesses allowed as well as load balancing issues that ensue when the demand for video objects is skew
- Cited by 3 (0 self) – Add To MetaCart

