Titlebook: Machine Learning and Data Mining in Pattern Recognition; 14th International C Petra Perner Conference proceedings 2018 Springer Internation

迷住

使迷惑

利用

miracle

Automatic Rail Flaw Localization and Recognition by Featureless Ultrasound Signal Analysis,CUDA implementation of B-scan segments comparison possesses the possibility to process big amounts of data. The experiments have shown that all rail flaws have been localized correctly and cross-validation ROC-score = 0.82 for the rail flaw recognition has been reached.

Seizure

Social Media Sentiment Analysis Based on Domain Ontology and Semantic Mining, of topics and opinions contained in the post. Preliminary results show that the application of domain ontology can effectively identify the topic-opinion pairs, and according to the known polarity of posts can effectively classify the topic-opinion pairs. The accuracy of sentiment classification is increasing.

钩针织物

剧毒

A Deep Learning Based Automatic Severity Detector for Diabetic Retinopathy, results showed that increasing the batch size does not necessarily speed up the convergence of the gradient computations. Also, it demonstrated that the number and size of fully connected layers do not have a significant impact on the performance of the model.

DRILL

From Black-Box to White-Box: Interpretable Learning with Kernel Machines, in the second phase, we extract an interpretable data representation from the first phase, and learn a sparse linear model. Finally, we evaluate our approach on benchmark datasets, and demonstrate its usefulness in terms of interpretability by visualization.

TRUST

A Two-List Framework for Accurate Detection of Frequent Items in Data Streams,nd real workloads confirm this result. A theoretical analysis of space and time complexity for FSSA and its benchmark algorithms was performed. Finally, we showed that FSS2L framework can be naturally parallelized, leading to a linear decrease in the maximum frequency estimation error.

enmesh

,Mining Associations in Large Graphs for Dynamically Incremented Marked Nodes,requires us to segregate the marked nodes from the less important nodes and group them together using partitioning algorithms. We introduce an improved algorithm which allows for the efficient addition of new marked nodes to a partition without rerunning the algorithm on previously marked nodes.