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

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Simulated Iterative Classification A New Learning Procedure for Graph Labelingon Algorithm (ICA) is a simple, efficient and widely used method to solve this problem. It is representative of a family of methods for which inference proceeds as an iterative process: at each step, nodes of the graph are classified according to the . of their neighbors. We show that learning in th

PALMY

Graph-Based Discrete Differential Geometry for Critical Instance Filteringarning, and feature ranking. This paper proposes a graph-based discrete differential operator for detecting and eliminating competence-critical instances and class label noise from a training set in order to improve classification performance. Results of extensive experiments on artificial and real-

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Integrating Novel Class Detection with Classification for Concept-Drifting Data Streamseaming environment, where new classes may evolve. Traditional data stream classification techniques are not capable of recognizing novel class instances until the appearance of the novel class is manually identified, and labeled instances of that class are presented to the learning algorithm for tra

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Neural Networks for State Evaluation in General Game Playing, the agent is supposed to play well without human intervention. For this purpose, agent systems that use deterministic game tree search need to automatically construct a state value function to guide search. Successful systems of this type use evaluation functions derived solely from the game rules

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Dynamic Factor Graphs for Time Series Modelingprobabilities between hidden and observed variables, and factors modeling dynamical constraints on hidden variables. The DFG assigns a scalar energy to each configuration of hidden and observed variables. A gradient-based inference procedure finds the minimum-energy state sequence for a given observ

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調色板

Efficient Pruning Schemes for Distance-Based Outlier Detectiondetecting distance-based outliers, aimed at reducing execution time associated with the detection process. Our approach operates in two phases and employs three pruning rules. In the first phase, we partition the data into clusters, and make an early estimate on the lower bound of outlier scores. Ba

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