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The first Java GUI toolkit was the Abstract Window Toolkit (AWT), introduced with Java Development Kit (JDK) 1.0 as one component of Sun Microsystems' Java platform. The original AWT was a simple Java wrapper library around native (operating system-supplied) widgets such as menus, windows, and buttons.
For example, in Swing, only use JButton, never Button class. As mentioned above, the AWT core classes, such as Color and Font, are still used as-is in Swing. When drawing in Swing, use JPanel and override paintComponent(Graphics g) instead of using the AWT paint() methods. Before Java 6 Update 12, mixing Swing components and basic AWT widgets ...
Rule-based machine learning (RBML) is a term in computer science intended to encompass any machine learning method that identifies, learns, or evolves 'rules' to store, manipulate or apply. [ 1 ] [ 2 ] [ 3 ] The defining characteristic of a rule-based machine learner is the identification and utilization of a set of relational rules that ...
An artificial neural network's learning rule or learning process is a method, mathematical logic or algorithm which improves the network's performance and/or training time. Usually, this rule is applied repeatedly over the network.
Association rule learning is a rule-based machine learning method for discovering interesting relations between variables in large databases. It is intended to identify strong rules discovered in databases using some measures of interestingness. [ 1 ]
A stable learning algorithm is one for which the prediction does not change much when the training data is modified slightly. For instance, consider a machine learning algorithm that is being trained to recognize handwritten letters of the alphabet, using 1000 examples of handwritten letters and their labels ("A" to "Z") as a training set. One ...
On the consumption of the last input symbol, if one of the current states is a final state, the machine accepts the string. A string of length n can be processed in time O(ns 2), [15] and space O(s). Create multiple copies. For each n way decision, the NFA creates up to n−1 copies of the machine. Each will enter a separate state.
Learning to rank [1] or machine-learned ranking (MLR) is the application of machine learning, typically supervised, semi-supervised or reinforcement learning, in the construction of ranking models for information retrieval systems. [2] Training data may, for example, consist of lists of items with some partial order specified between items in ...