Difference between revisions of "Anomaly Detection"
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* [[...find outliers]] | * [[...find outliers]] | ||
* [[Capabilities]] | * [[Capabilities]] | ||
| + | ** [[Embedding]]: [[AI-Powered Search|Search]] ... [[Clustering]] ... [[Recommendation]] ... [[Anomaly Detection]] ... [[Classification]] ... [[Dimensional Reduction]] ... [[...find outliers]] | ||
* [[Internet of Things (IoT)]] | * [[Internet of Things (IoT)]] | ||
* [[Screening; Passenger, Luggage, & Cargo]] | * [[Screening; Passenger, Luggage, & Cargo]] | ||
Revision as of 14:11, 20 March 2023
Youtube search... ...Google search
- Case Studies
- ...find outliers
- Capabilities
- Embedding: Search ... Clustering ... Recommendation ... Anomaly Detection ... Classification ... Dimensional Reduction ... ...find outliers
- Internet of Things (IoT)
- Screening; Passenger, Luggage, & Cargo
- Time-Series Anomaly Detection Service at Microsoft | H. Ren, B. Xu, Y. Wang, C. Yi, C. Huang, X. Kou, T. Xing, M. Yang, J. Tong, and Q. Zhang
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Principal Component Analysis (PCA) Anomaly Detection
PCA-based anomaly detection - the vast majority of the data falls into a stereotypical distribution; points deviating dramatically from that distribution are suspect Keep it Simple : Machine Learning & Algorithms for Big Boys | Dinesh Chandrasekar
