Titlebook: Neural Information Processing; 27th International C Haiqin Yang,Kitsuchart Pasupa,Irwin King Conference proceedings 2020 Springer Nature Sw

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Monotonous

ethereal

An Empirical Study to Investigate Different SMOTE Data Sampling Techniques for Improving Software Rects a set of software code metrics from object-oriented software systems, which are then processed for feature selection method to choose an appropriate sample set of features using Wilcoxon rank test. Once obtaining the optimal set of code-metrics, a novel ANN classifier using 5 different hidden la

领带

Efficient Binary Multi-view Subspace Learning for Instance-Level Image Retrievalive optimization for learning a compact similarity-preserving binary code. The resulting binary codes demonstrate significant advantage in retrieval precision and computational efficiency at the cost of limited memory footprint. More importantly, our method is essentially an unsupervised learning sc

calorie

Hyper-Sphere Support Vector Classifier with Hybrid Decision Strategy the intersection can be approximately linearly classified, new test samples can be classified by standard optimal binary-SVM hyper-plane. If training samples of two classes in the intersection cannot be linearly classified, new test samples can be decided by introducing kernel function to get optim

Evolve

Knowledge Graph Embedding Based on Relevance and Inner Sequence of Relationste the effectiveness of the proposed KGERSR on standard FB15k-237 and WN18RR datasets, and it gives about 2% relative improvement over the state-of-the-art method in terms of .@1, and .@10. Furthermore, KGERSR has fewer parameters than ConmplEX and TransGate. These results indicate that our method c

DAMP

MrPC: Causal Structure Learning in Distributed Systemsperimental results on benchmark datasets show that the proposed .gains up to seven times faster than sequential . implementation. In addition, kernel functions outperform conventional linear causal modelling approach across different datasets.

mastopexy

Online Multi-objective Subspace Clustering for Streaming Dataal subspace clusters. The generated clusters in the proposed method are allowed to contain overlapping of objects. To establish the superiority of using multiple objectives, the proposed method is evaluated on three real-life and three synthetic data sets. The results obtained by the proposed method

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PrivRec: User-Centric Differentially Private Collaborative Filtering Using LSH and KDe hashing (LSH) and the teacher-student knowledge distillation (KD) techniques. A teacher model is trained on the original user data without privacy constraints, and a student model learns from the hidden layers of the teacher model. The published student model is trained without access to the origi

轻快来事

Simultaneous Customer Segmentation and Behavior Discoveryistribution to improve efficiency. We tested our model with synthetic data and applied the framework to real-supermarket data with different products, and showed that our results can be interpreted with common knowledge.