Titlebook: Machine Learning and Knowledge Discovery in Databases; European Conference, Wray Buntine,Marko Grobelnik,John Shawe-Taylor Conference proce

他很靈活

Urea508

Conference proceedings 2009in Bled, Slovenia, in September 2009. The 106 papers presented in two volumes, together with 5 invited talks, were carefully reviewed and selected from 422 paper submissions. In addition to the regular papers the volume contains 14 abstracts of papers appearing in full version in the Machine Learnin

領(lǐng)帶

crockery

persistence

盤旋

A Convex Method for Locating Regions of Interest with Multi-instance Learningvia key instance generation at the instance-level and bag-level, respectively. Our formulation can be solved efficiently with a cutting plane algorithm. Experiments show that the proposed methods can effectively locate ROIs, and they also achieve performances competitive with state-of-the-art algorithms on benchmark data sets.

Pseudoephedrine

Active Learning for Reward Estimation in Inverse Reinforcement Learninguse of our algorithm in higher dimensional problems, using both Monte Carlo and gradient methods. We present illustrative results of our algorithm in several simulated examples of different complexities.

煤渣

Simulated Iterative Classification A New Learning Procedure for Graph Labelingsimulating inference during learning. Several variants of the method are introduced. They are both simple, efficient and scale well. Experiments performed on a series of 7 datasets show that the proposed methods outperform representative state-of-the-art algorithms while keeping a low complexity.

LURE

On Feature Selection, Bias-Variance, and Bagging reduction in dimensionality) with the harm of increased bias (from eliminating some of the relevant features). If a variance reduction method like bagging is used, more (weakly) relevant features can be exploited and the most accurate feature set is usually larger. In many cases, the best performance is obtained by using all available features.

JOG