Search Results - "Ting, Kai Ming"

What Are Anomalies in a Network?

Authors: Ting, Kai Ming Zhuang, Zhong Pang, Guansong et al.

Source: ACM Transactions on Knowledge Discovery from Data; Jul2025, Vol. 19 Issue 6, p1-34, 34p

Revisiting streaming anomaly detection: benchmark and evaluation.

Authors: Cao, Yang Ma, Yixiao Zhu, Ye et al.

Source: Artificial Intelligence Review; Jan2025, Vol. 58 Issue 1, p1-24, 24p

A new distributional treatment for time series anomaly detection.

Authors: Ting, Kai Ming Liu, Zongyou Gong, Lei et al.

Source: VLDB Journal International Journal on Very Large Data Bases; May2024, Vol. 33 Issue 3, p753-780, 28p

Isolation Kernel Estimators.

Authors: Ting, Kai Ming Washio, Takashi Wells, Jonathan et al.

Source: Knowledge & Information Systems; Feb2023, Vol. 65 Issue 2, p759-787, 29p

A new filter for deformation-invariant persistence diagram.

Authors: Zhang, Kaifeng Zhang, Hang Ting, Kai Ming et al.

Source: Machine Learning; Oct2025, Vol. 114 Issue 10, p1-38, 38p

Isolation kernel: the X factor in efficient and effective large scale online kernel learning.

Authors: Ting, Kai Ming Wells, Jonathan R. Washio, Takashi

Source: Data Mining & Knowledge Discovery; Nov2021, Vol. 35 Issue 6, p2282-2312, 31p

A large language model for deriving spectral embeddings for accurate compound identification in mass spectrometry.

Authors: Xu, Yang Ma, Yixiao Xu, Weijie et al.

Source: Communications Chemistry. 11/4/2025, Vol. 8 Issue 1, p1-13. 13p.

A comparative study of data-dependent approaches without learning in measuring similarities of data objects.

Authors: Aryal, Sunil Ting, Kai Ming Washio, Takashi et al.

Source: Data Mining & Knowledge Discovery; Jan2020, Vol. 34 Issue 1, p124-162, 39p

Simple supervised dissimilarity measure: Bolstering iForest-induced similarity with class information without learning.

Authors: Wells, Jonathan R. Aryal, Sunil Ting, Kai Ming

Source: Knowledge & Information Systems; Aug2020, Vol. 62 Issue 8, p3203-3216, 14p

On the Application of ROC Analysis to Predict Classification Performance Under Varying Class Distributions.

Authors: Webb, Geoffrey I. Ting, Kai Ming

Source: Machine Learning; January 2005, Vol. 58 Issue 1, p25-32, 8p

Corrigendum to "Kernel-Bounded Clustering: Achieving the Objective of Spectral Clustering without Eigendecomposition" [Artificial Intelligence 350 (2026) 104440].

Authors: Zhang, Hang Ting, Kai Ming Zhu, Ye

Source: Artificial Intelligence; Feb2026, Vol. 351, pN.PAG-N.PAG, 1p

Kernel-bounded clustering: Achieving the objective of spectral clustering without eigendecomposition.

Authors: Zhang, Hang Ting, Kai Ming Zhu, Ye

Source: Artificial Intelligence; Jan2026, Vol. 350, pN.PAG-N.PAG, 1p

Modeling Structural Deviation in 10-K Risk Factors: A Semantic Anomaly Detection and Explainable AI Approach.

Authors: Sun, Fang He, Shuangjiang Wang, Ruiqi et al.

Source: Risks. Apr2026, Vol. 14 Issue 4, p87. 25p.

A double-layer ensemble framework for rubber plantation mapping using multi-source data in the google earth engine: a case study of the southwestern border region of China.

Authors: Wang, Hui Li, Jie Wang, Jinliang et al.

Source: International Journal of Digital Earth. Dec2025, Vol. 18 Issue 1, p1-33. 33p.

P‐96: Importance of Data Structure Change by Applying Domain Knowledge in Industrial Tabular Data.

Authors: Hwang, Jinha Lee, Sanghee

Source: SID Symposium Digest of Technical Papers; Jun2023, Vol. 54 Issue 1, p1540-1543, 4p

Defining city boundaries through percolation theory: the case of the mexican urban system.

Alternate Title: Límites urbanos mediante la teoría de la percolación: el caso del sistema urbano mexicano.

Authors: Tapia-McClung, Rodrigo Alberto Montejano-Escamilla, Jorge Alberto Caudillo-Cos, Camilo

Source: Economía, Sociedad y Territorio. Sep-Dec2023, Vol. 23 Issue 73, p753-778. 26p.

An instance-weighting method to induce cost-sensitive trees.

Authors: Ting, Kai Ming

Source: IEEE Transactions on Knowledge & Data Engineering; May/June 2002, Vol. 14 Issue 3, p659-665, 7p

A Generic Ensemble Approach to Estimate Multidimensional Likelihood in Bayesian Classifier Learning.

Authors: Aryal, Sunil Ting, Kai Ming

Source: Computational Intelligence; Aug2016, Vol. 32 Issue 3, p458-479, 22p

A new simple and efficient density estimator that enables fast systematic search.

Authors: Wells, Jonathan R. Ting, Kai Ming

Source: Pattern Recognition Letters; May2019, Vol. 122, p92-98, 7p

Is it possible to find the single nearest neighbor of a query in high dimensions?

Authors: Ting, Kai Ming Washio, Takashi Zhu, Ye et al.

Source: Artificial Intelligence; Nov2024, Vol. 336, pN.PAG-N.PAG, 1p