C4.5: programs for machine learning
C4.5: programs for machine learning
The nature of statistical learning theory
The nature of statistical learning theory
Computing depth contours of bivariate point clouds
Computational Statistics & Data Analysis - Special issue on classification
LOF: identifying density-based local outliers
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Mining distance-based outliers in near linear time with randomization and a simple pruning rule
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Manifold-ranking based image retrieval
Proceedings of the 12th annual ACM international conference on Multimedia
Enhancing relevance feedback in image retrieval using unlabeled data
ACM Transactions on Information Systems (TOIS)
Query-Sensitive Similarity Measure for Content-Based Image Retrieval
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
ELKI: A Software System for Evaluation of Subspace Clustering Algorithms
SSDBM '08 Proceedings of the 20th international conference on Scientific and Statistical Database Management
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
BALAS: Empirical Bayesian learning in the relevance feedback for image retrieval
Image and Vision Computing
Mass estimation and its applications
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Multi-dimensional Mass Estimation and Mass-based Clustering
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Relevance feature mapping for content-based multimedia information retrieval
Pattern Recognition
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This paper introduces mass estimation--a base modelling mechanism that can be employed to solve various tasks in machine learning. We present the theoretical basis of mass and efficient methods to estimate mass. We show that mass estimation solves problems effectively in tasks such as information retrieval, regression and anomaly detection. The models, which use mass in these three tasks, perform at least as well as and often better than eight state-of-the-art methods in terms of task-specific performance measures. In addition, mass estimation has constant time and space complexities.