Graph-based embeddingDiscriminative embeddingFeature weightingSupervised learningSemi-supervised learningPattern recognitionRecently, several inductive and flexible nonlinear data projection methods for graph-b
The approaches for the UGCs’ popularity prediction can be divided into two categories: point process methods and feature-based methods (Mishra et al., 2016; Wu et al., 2018). Point process methods are used to exploit popularity evolution patterns, which regard the popularity cumulation of UGCs...
A more complex model for music recommendation based on metric embedding was proposed in Chen et al. (2012). Up to about the year 2015, the literature on session-based recommendation was rather sparse, with some research published on non-public datasets from time to time, e.g., Garcin et ...
The feature augmentation method based on IFR and the few-shot Learning framework with IFR based feature augmentation are introduced in Sect. “Methods”. The experimental results and analysis are presented in Sect. “Results and Discussion”. Conclusions and future work are presented in Sect. “...
7.Spectral Embedding Spectral embedding is a dimension reduction technique that also serves as a feature selector by finding a lower-dimensional representation of the data while preserving the spectral properties of the data. 4.1.1.6.1.2.non-dimension reduction based methods ...
neural network methods in modeling the tabular data, showcasing consistent interpretability in its cross-feature embedding module for medical diagnosis ... M Ye,Y Yu,Z Shen,... - 《IEEE Transactions on Knowledge & Data Engineering》 被引量: 0发表: 0年 ASENN: attention-based selective embeddin...
Methods The Chinese long text classification model based on feature-enhanced text-inception consists of four parts: a word embedding layer, a feature extraction layer, a feature enhancement layer, and output layer. First, the original text is pre-processed and word embedding layer is put to vecto...
EMBEDDING YT-DLP yt-dlp makes the best effort to be a good command-line program, and thus should be callable from any programming language. Your program should avoid parsing the normal stdout since they may change in future versions. Instead, they should use options such as -J, --print, ...
Our work is based on [8] and achieve significant progress. C. Unsupervised re-ID Due to the unsupervised methods do not rely on labeled samples, the performance of these methods are poor relatively. In this work, we will pay our attention on one-shot learning. D. Progressive Learning ...
Traditionally, anti-malware engines relied on signature-based and heuristic-based methods to detect and block malware before they performed any damage. On the one hand, signature-based methods identify malware by comparing its code with the code of known malware that have already encountered, analyze...