Machine Learning
Machine learning algorithms improve task performance as the size of the training set increases
Machine learning algorithms “learn” from data. They improve performance at a task based on experience. For example, the accuracy of the predictions made by a neural network will typically improve as you increase the number of samples available to train the network. Many machine learning algorithms develop their decision making rules based on training examples. This is known as “supervised” learning. “Unsupervised learning” algorithms draw inference using unlabeled data.
MATLAB and Statistics Toolbox can be used to solve both supervised and unsupervised machine learning problems.
Statistics Toolbox includes a wide variety of machine learning algorithms including boosted and bagged decision trees, K-means and hierarchical clustering, K-nearest neighbor search, Gaussian mixtures, the expectation maximimization algorithm, and hidden Markov models.
Neural Network Toolbox provides tools for designing, implementing, visualizing, and simulating neural networks including feedforward networks, radial basis networks, and self organizing maps.
Examples and How To
- Getting Started with Classification Using MATLAB (Featuring Machine Learning) (Webinar)
- Getting Started with Neural Network Toolbox (Video)
- Use K-means and Hierarchical Clustering to Find Natural Patterns in Data (Example)
- Maximum Likelihood Estimation of Gaussian Mixtures Using the Expectation Maximization Algorithm (File Exchange)
Software Reference
- Ensemble Methods (Including Boosted and Bagged Decision Trees) (Documentation)
- fitensemble: Train a Boosted or Bagged Decision Tree (Function)
- kmeans: K-means Clustering (Function)
- knnsearch: Find K-nearest Neighbors (Function)
- Using Hidden Markov Models (Documentation)
- hmmtrain: Estimate Hidden Markov Model Parameters from Emissions (Function)
- svmtrain: Train a Support Vector Machine Classifier (Function)
See also: random number, smoothing, data analysis, mathematical modeling
Watch Data Driven Fitting with MATLAB (Webinar)
