Titlebook: Machine Learning and Data Mining in Pattern Recognition; 10th International C Petra Perner Conference proceedings 2014 Springer Internation

新義

Callus

Manifold Learning in Data Mining Tasks to the use of many methods for solving these tasks. To avoid these phenomena, various Representation learning algorithms are used as a first key step in solutions of these tasks to transform the original high-dimensional data into their lower-dimensional representations so that as much information

cushion

Adaptive Multiple-Resolution Stream Clustering. Due to continuously evolving nature of the stream, it is crucial that the algorithm autonomously detects clusters of arbitrary shape, with different densities, and varying number of clusters. Although available density-based stream clustering are able to detect clusters with arbitrary shapes and v

extemporaneous

碎片

Minimizing Cluster Errors in LP-Based Nonlinear Classificationlasses. Good numerical results have been reported; however, there remain some concerns regarding prediction ability when dealing with large data bases. This paper introduces clustering, which decreases the number of variables in the linear programming models that need be solved at each iteration. Pr

態(tài)學(xué)

CBOF: Cohesiveness-Based Outlier Factor A Novel Definition of Outlier-nessin a dataset that are remarkably different from the rest of the population. Knowledge about such anomalies is of immense practical value, such as for detection of fraud in financial transactions, for finding faulty equipment in industrial plants, and for detection of security breaches in a computer

卜聞

A New Measure of Outlier Detection Performancepper bound for false alarm rate while measuring the detection power, which proves to be a difficult task. In this paper we introduce a new measure of outlier detection performance .. as the harmonic mean of the power and unit minus false alarm rate. The .. maximizes the detection power by minimizing

評(píng)論性

培養(yǎng)

裙帶關(guān)系

Towards Time Series Classification without Human Preprocessingn assistance to extract characteristic, aligned patterns of equal length and scaling. Human assistance is not cost-effective. We propose our .similarity metric that extracts, scales, and aligns segments from a query to a sample time series. This simplifies the classification of time series as produc