Titlebook: Machine Learning and Knowledge Discovery in Databases; European Conference, Toon Calders,Floriana Esposito,Rosa Meo Conference proceedings

死亡率

Reliability Maps: A Tool to Enhance Probability Estimates and Improve Classification Accuracyning calibrated multi-class probabilities from two-class probability scores. The LS-ECOC method approaches this by performing least-squares fitting over a suitable error-correcting output code matrix, where the optimisation resolves potential conflicts in the input probabilities. While this gives al

Expertise

Causal Clustering for 2-Factor Measurement Modelsy used to make such inferences is to use the values of variables that can be measured directly that are thought to be “indicators” of the latent variables of interest, together with a hypothesized causal graph relating the latent variables to their indicators. To use the data on the indicators to dr

细胞膜

Support Vector Machines for Differential Predictionroblems that are not easily addressed using standard machine learning approaches and, in particular, there is growing interest in .. In this type of task we are interested in producing a classifier that specifically characterizes a subgroup of interest by maximizing the difference in predictive perf

Obligatory

CONE

Mining Top-K Largest Tiles in a Data Streamse large tiles is an important pattern mining problem since tiles with a large area describe a large part of the database. In this paper, we introduce the problem of mining top-. largest tiles in a data stream under the sliding window model. We propose a candidate-based approach which summarizes the

Nucleate

连接

易发怒

骂人有污点

FILTA: Better View Discovery from Collections of Clusterings via Filteringr user navigation and refinement. However, the effectiveness of meta-clustering is highly dependent on the distribution of the base clusterings and open challenges exist with regard to its stability and noise tolerance. In this paper we propose a simple and effective filtering algorithm (FILTA) that

Feature

Nonparametric Markovian Learning of Triggering Kernels for Mutually Exciting and Mutually Inhibiting not only mutually-exciting but can also exhibit inhibitive patterns. We focus on nonparametric learning and propose a novel algorithm called MEMIP (Markovian Estimation of Mutually Interacting Processes) that makes use of polynomial approximation theory and self-concordant analysis in order to lear