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

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

叫喊

Mining Spatial Co-location Patterns with Dynamic Neighborhood Constrainta greedy algorithm for mining co-location patterns with dynamic neighborhood constraint. The experimental evaluation on a real world data set shows that our algorithm has a better capability than the previous approach on finding co-location patterns together with the consideration of the distribution of data set.

無價(jià)值

Classifier Chains for Multi-label Classificationlexity. Empirical evaluation over a broad range of multi-label datasets with a variety of evaluation metrics demonstrates the competitiveness of our chaining method against related and state-of-the-art methods, both in terms of predictive performance and time complexity.

熱情贊揚(yáng)