Titlebook: Machine Learning and Knowledge Discovery in Databases; European Conference, Albert Bifet,Michael May,Myra Spiliopoulou Conference proceedin

FUSE

Logic-Based Incremental Process Miningthe ability to express/learn complex conditions on the involved tasks, are also desirable. First-order logic provides a single comprehensive and powerful framework for supporting all of the above. This paper presents a First-Order Logic incremental method for inferring process models. Its efficiency

無(wú)效

Watch-It-Next: A Contextual TV Recommendation System program the device. We present an empirical evaluation of several recommendation methods over large-scale, real-life TV viewership data. Our extentions of common state-of-the-art recommendation methods, exploiting the current watching context, demonstrate a significant improvement in recommendation quality.

CLASH

Discovering Neutrinos Through Data Analytics background ratio in the initial data (trigger level) is roughly 1:.. The overall process was embedded in a multi-fold cross-validation to control its performance. A subsequent regularized unfolding yields the sought after neutrino energy spectrum.

Sputum

Clustering by Intent: A Semi-Supervised Method to Discover Relevant Clusters Incrementally we consistently get relevant results and at interactive time scales. This paper describes the method and demonstrates its superior ability using publicly available datasets. For automated evaluation, we devised a unique cluster evaluation framework to match the business user’s utility.

flex336

Learning Detector of Malicious Network Traffic from Weak Labels We demonstrate that an accurate detector can be obtained from the collected security intelligence data by using a Multiple Instance Learning algorithm tailored to the Neyman-Pearson problem. We provide a thorough experimental evaluation on a large corpus of network communications collected from various company network environments.

TEN

浪費(fèi)時(shí)間

Robust Representation for Domain Adaptation in Network Securityved by relying on a self-similarity matrix computed for each bag. In our experiments, we will show that the representation is effective for training detector of malicious traffic in large corporate networks. Compared to the case without domain adaptation, the recall of the detector improves from 0.81 to 0.88 and precision from 0.998 to 0.999.

Functional

PACK

NAG

Data-Driven Exploration of Real-Time Geospatial Text Streamselevant information from irrelevant chatter using unsupervised and supervised methods alike. This allows the structuring of requested information as well as the incorporation of unexpected events into a common overview of the situation. A special focus is put on the interplay of algorithms, visualization, and interaction.