Titlebook: Machine Learning: ECML 2005; 16th European Confer João Gama,Rui Camacho,Luís Torgo Conference proceedings 2005 Springer-Verlag Berlin Heide

脆弱带来

Obliterate

曲解

我们的面粉

干涉

许可

Data Streams and Data Synopses for Massive Data Sets (Invited Talk)eveloping algorithmic techniques for data stream models. We will discuss some of the research work that has been done in the field, and provide a decades’ perspective to data synopses and data streams.

Oafishness

Estimation of Mixture Models Using Co-EMt mixture component. We derive an algorithm that maximizes this criterion. Empirically, we observe that the resulting clustering method incurs a lower cluster entropy than regular EM for web pages, research papers, and many text collections.

cauda-equina

Nonrigid Embeddings for Dimensionality Reductionaffine rigidity and edge lengths to obtain isometric embeddings. An implemented algorithm is fast, accurate, and industrial-strength: Experiments with problem sizes spanning four orders of magnitude show .(.) scaling. We demonstrate with speech data.

languor

Hybrid Algorithms with Instance-Based Classification compare the overlap in errors and the statistical bias and variance of the hybrids, their parent algorithms, and a plain instance-based learner. We observe that the successful hybrid algorithms have a lower statistical bias component in the error than their parent algorithms; the fewer errors they make are also less systematic.

negligence

Recent Advances in Mining Time Series Data.– New algorithms/definitions..– The migration from static problems to online problems..– New areas and applications of time series data mining..I will end the talk with a discussion of “what’s left to do” in time series data mining.