Titlebook: Machine Learning and Knowledge Discovery in Databases; European Conference, Peter A. Flach,Tijl Bie,Nello Cristianini Conference proceeding

安心地散步

coagulation

的闡明

agglomerate

Analyzing Text and Social Network Data with Probabilistic Modelsnce, history, medicine, and more. This talk will present an overview of recent work using probabilistic latent variable models to analyze such data. Latent variable models have a long tradition in data analysis and typically hypothesize the existence of simple unobserved phenomena to explain relativ

conquer

Discovering Descriptive Tile Trees are difficult to read, or return so many results interpretation becomes impossible. Here, we study a fully automated approach for mining easily interpretable models for binary data. We model data hierarchically with noisy tiles—rectangles with significantly different density than their parent tile.

處理

Efficient Discovery of Association Rules and Frequent Itemsets through Sampling with Tight Performanons. Exact algorithms for these problems exist and are widely used, but their running time is hindered by the need of scanning the entire dataset, possibly multiple times. High quality approximations of FI’s and AR’s are sufficient for most practical uses, and a number of recent works explored the a

Brochure

Smoothing Categorical Data one wants to see the large scale structure, one should somehow subtract this smaller scale structure from the model..While for some kinds of model – such as boosted classifiers – it is easy to see the “important” components, for many kind of models this is far harder, if at all possible. In such ca

倔強不能

Cloudburst

Bayesian Network Classifiers with Reduced Precision Parametersaph and a set of conditional probabilities associated with the nodes of the graph. These conditional probabilities are also referred to as parameters of the BNCs. According to common belief, these classifiers are insensitive to deviations of the conditional probabilities under certain conditions. Th

organism