File:Kernel Machine.svg
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Size of this PNG preview of this SVG file: 512 × 233 pixels. Other resolutions: 320 × 146 pixels | 640 × 291 pixels | 800 × 364 pixels | 1,024 × 466 pixels | 1,280 × 583 pixels.
Original file (SVG file, nominally 512 × 233 pixels, file size: 5 KB)
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Summary
| Description |
English: Kernel machines are used to compute non-linearly separable functions into a higher dimension linearly separable function.
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| Date | |
| Source | Own work |
| Author | Alisneaky, svg version by User:Zirguezi |
| Other versions |
Licensing
I, the copyright holder of this work, hereby publish it under the following license:
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This file is licensed under the Creative Commons Attribution-Share Alike 4.0 International license. | |
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File history
Click on a date/time to view the file as it appeared at that time.
| Date/Time | Thumbnail | Dimensions | User | Comment | |
|---|---|---|---|---|---|
| current | 18:07, 8 February 2017 | | 512 × 233 (5 KB) | SemperVinco | Optimized code |
| 01:54, 20 November 2016 |  | 512 × 232 (52 KB) | Ninjatacoshell | 1. Made ellipses into circles and made rectangles into squares. Filled open circles with white. Shifted some of the circles. 2. Centered arrow and Ø (horizontally and vertically). 3. Simplified the curve of the red line on the left. Made red lines the... | |
| 12:50, 30 March 2016 |  | 512 × 232 (12 KB) | Zirguezi | Better code | |
| 12:49, 30 March 2016 |  | 512 × 232 (11 KB) | Zirguezi | User created page with UploadWizard |
File usage
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- Action model learning
- Active learning (machine learning)
- AdaBoost
- Anomaly detection
- Artificial neural network
- Association rule learning
- Autoencoder
- BIRCH
- Backpropagation
- Bias–variance tradeoff
- Boosting (machine learning)
- Bootstrap aggregating
- CURE algorithm
- Canonical correlation
- Cluster analysis
- Computational learning theory
- Conditional random field
- Conference on Neural Information Processing Systems
- Convolutional neural network
- DBSCAN
- Data mining
- Data science
- Decision tree learning
- DeepDream
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- Deep learning
- Deeplearning4j
- Dimensionality reduction
- Discriminative model
- Empirical risk minimization
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- Expectation–maximization algorithm
- Factor analysis
- Feature engineering
- Feature learning
- Fuzzy clustering
- Gradient boosting
- Grammar induction
- Graphical model
- H2O (software)
- Hidden Markov model
- Hierarchical clustering
- Independent component analysis
- International Conference on Machine Learning
- K-SVD
- K-means clustering
- K-nearest neighbors algorithm
- Kernel method
- Kernel perceptron
- Learning to rank
- Linear discriminant analysis
- Local outlier factor
- Logic learning machine
- Logistic model tree
- Long short-term memory
- Machine learning
- Mean shift
- Multilayer perceptron
- Multiple kernel learning
- Naive Bayes classifier
- Naive Bayes spam filtering
- Neighbourhood components analysis
- Non-negative matrix factorization
- OPTICS algorithm
- Occam learning
- Online machine learning
- Pattern recognition
- Perceptron
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- Probabilistic classification
- Probably approximately correct learning
- Q-learning
- Random forest
- Recurrent neural network
- Regression analysis
- Reinforcement learning
- Relevance vector machine
- Restricted Boltzmann machine
- Sample complexity
- Self-organizing map
- Semi-supervised learning
- State–action–reward–state–action
- Statistical classification
- Statistical learning theory
- Structured prediction
- Supervised learning
- Support-vector machine
- T-distributed stochastic neighbor embedding
- Temporal difference learning
- Unsupervised learning
- Vanishing gradient problem
- Vapnik–Chervonenkis theory
- Word embedding
- User:Kazkaskazkasako/Books/EECS
- User:Liorrokach/sandbox
- User:Mneykov/sandbox
- User:Pixtonc/sandbox
- User:Scott.linderman/sandbox
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- Apprendimento automatico
- Regressione lineare
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- Clustering
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- Analisi delle componenti principali
- Classificazione
- Soft computing
- Albero di decisione
- Macchine a vettori di supporto
- Rete bayesiana
- Self-Organizing Map
- K-means
- Modello di Markov nascosto
- Percettrone
- Analisi delle componenti indipendenti
- Analisi fattoriale
- Dbscan
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