T
Tie Li
Researcher at University of Electronic Science and Technology of China
Publications - 6
Citations - 233
Tie Li is an academic researcher from University of Electronic Science and Technology of China. The author has contributed to research in topics: Artificial neural network & Diagonal matrix. The author has an hindex of 3, co-authored 5 publications receiving 161 citations.
Papers
More filters
Journal ArticleDOI
Improving malicious URLs detection via feature engineering: Linear and nonlinear space transformation methods
TL;DR: The results showed that the proposed methods significantly improved the efficiency and performance of certain classifiers, such as k-Nearest Neighbor, Support Vector Machine, and neural networks.
Journal ArticleDOI
Classifying With Adaptive Hyper-Spheres: An Incremental Classifier Based on Competitive Learning
TL;DR: Adaptive hyper-sphere (AdaHS) as mentioned in this paper is an adaptive incremental classifier, which incorporates competitive training with a border zone, and has strong capability of local learning like instance-based algorithms, but free from slow searching speed and excessive memory consumption.
Journal ArticleDOI
Jie Ke versus AlphaGo: A ranking approach using decision making method for large-scale data with incomplete information
TL;DR: A new ranking method for large-scale Go players by means of incomplete fuzzy pair-wise comparison matrix whose priority vector is derived using a cosine similarity measure is proposed.
Journal ArticleDOI
A fast diagonal distance metric learning approach for large-scale datasets
TL;DR: A MapReduce framework to build triplets is designed, which are encapsulations of triple data points used for the optimization problem, and a weighting mechanism for triplets according to their contributions to the whole distance distortion is developed.
Journal ArticleDOI
Study on Multi-Agent Based Simulation of Team Machine Learning
TL;DR: The organization theories of human society, such as cooperation and competition, to machine learning are introduced and the results show that the overall performance of team learning can be promoted dramatically and coordination structure of the machines can be optimized.