Keywords
Computer scienceArtificial intelligenceSeries (stratigraphy)ScalabilityMachine learningDecision treeTime seriesVotingParametric statisticsData miningPattern recognition (psychology)Mathematics
Affiliated Institutions
Related Publications
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
Object detection performance, as measured on the canonical PASCAL VOC dataset, has plateaued in the last few years. The best-performing methods are complex ensemble systems that...
Unsupervised Feature Learning via Non-parametric Instance Discrimination
Neural net classifiers trained on data with annotated class labels can also capture apparent visual similarity among categories without being directed to do so. We study whether...
Object Detection with Discriminatively Trained Part-Based Models
We describe an object detection system based on mixtures of multiscale deformable part models. Our system is able to represent highly variable object classes and achieves state-...
MobileNetV2: Inverted Residuals and Linear Bottlenecks
In this paper we describe a new mobile architecture, MobileNetV2, that improves the state of the art performance of mobile models on multiple tasks and benchmarks as well as acr...
Part-Based Statistical Models for Object Classification and Detection
We propose using simple mixture models to define a set of mid-level binary local features based on binary oriented edge input. The features capture natural local structures in t...
Publication Info
- Year
- 2005
- Type
- book-chapter
- Pages
- 478-485
- Citations
- 34
- Access
- Closed
External Links
Social Impact
Altmetric
PlumX Metrics
Social media, news, blog, policy document mentions
Citation Metrics
34
OpenAlex
Cite This
Pierre Geurts,
Louis Wehenkel
(2005).
Segment and Combine Approach for Non-parametric Time-Series Classification.
Lecture notes in computer science
, 478-485.
https://doi.org/10.1007/11564126_48
Identifiers
- DOI
- 10.1007/11564126_48