File:Kernel Machine.svg
From Wikipedia, the free encyclopedia
Size of this PNG preview of this SVG file: 512 × 232 pixels. Other resolutions: 320 × 145 pixels | 640 × 290 pixels | 800 × 363 pixels | 1,024 × 464 pixels | 1,280 × 580 pixels.
Original file (SVG file, nominally 512 × 232 pixels, file size: 52 KB)
 | This is a file from the Wikimedia Commons. Information from its description page there is shown below. Commons is a freely licensed media file repository. You can help. |
Summary[edit]
| Description |
English: Kernel machines are used to compute a non-linearly separable functions into a higher dimension linearly separable function.
|
| Date | |
| Source | Own work |
| Author | Alisneaky, svg version by User:Zirguezi |
| Other versions |
Licensing[edit]
|
I, the copyright holder of this work, hereby publish it under the following license:
|
File history
Click on a date/time to view the file as it appeared at that time.
| Date/Time | Thumbnail | Dimensions | User | Comment | |
|---|---|---|---|---|---|
| current | 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
More than 100 pages link to this file. The following list shows the first 100 page links to this file only. A full list is available.
- Action model learning
- Anomaly detection
- Artificial neural network
- Association rule learning
- Autoencoder
- BIRCH
- Backpropagation
- Bias–variance tradeoff
- Boosting (machine learning)
- Bootstrap aggregating
- Canonical correlation
- Cluster analysis
- Computational learning theory
- Conditional random field
- Convolutional neural network
- DBSCAN
- Data mining
- Decision tree learning
- Deep belief network
- Deep learning
- Deeplearning4j
- Dimensionality reduction
- Empirical risk minimization
- Ensemble learning
- Error Tolerance (PAC learning)
- Expectation–maximization algorithm
- Extreme learning machine
- Factor analysis
- Feature engineering
- Feature learning
- Feature selection
- Fuzzy clustering
- Grammar induction
- H2O (software)
- Hidden Markov model
- Hierarchical clustering
- Hoshen–Kopelman algorithm
- International Conference on Machine Learning
- K-SVD
- K-means clustering
- K-nearest neighbors algorithm
- Kernel method
- Kernel perceptron
- Learning to rank
- List of datasets for machine learning research
- Local outlier factor
- Logic learning machine
- Logistic model tree
- Machine learning
- Mean shift
- Multilayer perceptron
- Multiple instance learning
- Multiple kernel learning
- Naive Bayes classifier
- Naive Bayes spam filtering
- Neighbourhood components analysis
- OPTICS algorithm
- Occam learning
- Online machine learning
- Out-of-bag error
- Pattern recognition
- Perceptron
- Platt scaling
- Probabilistic classification
- Probably approximately correct learning
- Q-learning
- Random field
- Random forest
- Recurrent neural network
- Reinforcement learning
- Relevance vector machine
- Restricted Boltzmann machine
- Sample complexity
- Self-organizing map
- Semi-supervised learning
- Sparse dictionary 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
- Word2vec
- Word embedding
- User:Anonymousbadger/sandbox
- User:Kazkaskazkasako/Books/EECS
- User:Like.liberation/sandbox
- User:Liorrokach/sandbox
- User:Mneykov/sandbox
- User:Pakoch/sandbox
- User:Pixtonc/sandbox
- User:Psneog/sandbox
- User:Quantares/sandbox
- User:Scott.linderman/sandbox
- User:TonyWang0316/sandbox
Global file usage
The following other wikis use this file:
- Usage on az.wikipedia.org
- Usage on el.wikipedia.org
- Usage on ja.wikipedia.org
- Usage on nl.wikipedia.org
- Usage on sr.wikipedia.org
- Usage on uk.wikipedia.org
- Добування даних
- Машинне навчання
- Штучна нейронна мережа
- Кластеризація методом к–середніх
- Самоорганізаційна Карта Кохонена
- Рекурентні нейронні мережі
- Кластерний аналіз
- Перцептрон
- Багатошаровий перцептрон Румельхарта
- Теорія розпізнавання образів
- Задача класифікації
- Прихована марковська модель
- Ядрові методи
- Метод опорних векторів
- Наївний баєсів класифікатор
- ЕМ-алгоритм
- Random forest
- Шаблон:Машинне навчання
- Навчання без учителя
- OPTICS
- Виявлення аномалій
- Розклад невід'ємних матриць
- Навчання з учителем
- Навчання з підкріпленням
- Нейронна мережа прямого поширення
- Метод найближчих k-сусідів
- Глибинне навчання
- Навчання ознак
- Обмежена машина Больцмана
- Глибинна мережа переконань
- Згорткова нейронна мережа
- Вибір ознак
- Автокодувальник
- Проектування ознак
- Q-навчання
- Напівавтоматичне навчання
- Проблема зникання градієнту
View more global usage of this file.
