Machine Learning and Data Mining in Pattern Recognition
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Summary
Data mining techniques that use metrics defined on sets of partitions of finite sets such as decision tree induction, feature selection, and data discretization are discussed.
- Type
- book
- Published
- 2019-01-07
- Cited by
- 85
- References
- 320
- OpenAlex
- https://openalex.org/W634295876
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:52828107
Keywords
St petersburg, Library science, Artificial intelligence, Computer science, Geography
References
- Clustering For Data Mining: A Data Recovery Approach (Chapman & Hall/Crc Computer Science)
- Experiencing the Shotgun Distance for Time Series Analysis
- 3D face recognition using photometric stereo
- Harvesting real time traffic information from Twitter
- Real-time Pattern Isolation and Recognition Over Immersive Sensor Data Streams
- A data clustering approach based on universal gravity rule
- Twitter archives and the challenges of "Big Social Data" for media and communication research
- Entropy and Information Theory
- Clickstream clustering using weighted longest common subsequences
- Programs for Machine Learning
- Laplacian of graphs and algebraic connectivity
- Relating Clustering Stability to Properties of Cluster Boundaries
- Useful Clustering Outcomes from Meaningful Time Series Clustering
- Hot topic detection in local areas using Twitter and Wikipedia
- Clustering of time series subsequences is meaningless: implications for previous and future research
- Vision-based traffic accident detection using matrix approximation
- A comparison of event models for naive bayes text classification
- Statistical Comparisons of Classifiers over Multiple Data Sets
- Geographic Information Analysis
- Using output codes to boost multiclass learning problems
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- A reinforcement learning approach for the scheduling of live migration from under utilised hosts
- Evolving the SVM model based on a hybrid method using swarm optimization techniques in combination with a genetic algorithm for medical diagnosis
- Machine learning friendly set version of Johnson–Lindenstrauss lemma
- Predicting Foreground Object Ambiguity and Efficiently Crowdsourcing the Segmentation(s)
- Rate of change analysis for interestingness measures
- Data complexity meta-features for regression problems
- Visual Analysis of Brain Networks Using Sparse Regression Models
- Estimating an Individual’s Probability of Revision Surgery After Knee Replacement: A Comparison of Modeling Approaches Using a National Data Set
- A review on distance based time series classification
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- Multimodal Radiomic Features for the Predicting Gleason Score of Prostate Cancer
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