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This type of learning has three primary components: the agent (the learner or decision maker), the environment (everything the agent interacts with) and actions (what the agent can do).
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With reinforcement learning, the algorithm discovers through trial and error which actions yield the greatest rewards. Reinforcement learning is often used for robotics, gaming and navigation. Early examples of this include identifying a person's face on a web cam. Semisupervised learning is useful when the cost associated with labeling is too high to allow for a fully labeled training process.
This type of learning can be used with methods such as classification, regression and prediction. But it uses both labeled and unlabeled data for training – typically a small amount of labeled data with a large amount of unlabeled data (because unlabeled data is less expensive and takes less effort to acquire).
Semisupervised learning is used for the same applications as supervised learning. These algorithms are also used to segment text topics, recommend items and identify data outliers. Popular techniques include self-organizing maps, nearest-neighbor mapping, k-means clustering and singular value decomposition. Or it can find the main attributes that separate customer segments from each other. For example, it can identify segments of customers with similar attributes who can then be treated similarly in marketing campaigns. Unsupervised learning works well on transactional data. The goal is to explore the data and find some structure within. The system is not told the "right answer." The algorithm must figure out what is being shown. Unsupervised learning is used against data that has no historical labels. For example, it can anticipate when credit card transactions are likely to be fraudulent or which insurance customer is likely to file a claim. Supervised learning is commonly used in applications where historical data predicts likely future events.
Through methods like classification, regression, prediction and gradient boosting, supervised learning uses patterns to predict the values of the label on additional unlabeled data. The learning algorithm receives a set of inputs along with the corresponding correct outputs, and the algorithm learns by comparing its actual output with correct outputs to find errors. For example, a piece of equipment could have data points labeled either “F” (failed) or “R” (runs). Supervised learning algorithms are trained using labeled examples, such as an input where the desired output is known.
They learn from previous computations to produce reliable, repeatable decisions and results. It was born from pattern recognition and the theory that computers can learn without being programmed to perform specific tasks researchers interested in artificial intelligence wanted to see if computers could learn from data. The iterative aspect of machine learning is important because as models are exposed to new data, they are able to independently adapt. Because of new computing technologies, machine learning today is not like machine learning of the past.