In concept learning, the task typically starts with a set of training examples, where each example is labeled with the corresponding class or category it belongs to. These labeled examples are used as the basis for the learning algorithm to derive general rules or patterns that can help classify new, unseen instances.
Concept space refers to the set of all possible concepts or categories that the learning algorithm can potentially learn from the training data. The algorithm explores this concept space to identify patterns and generalizations that can discriminate between different classes output labels. The goal is to develop a model that can accurately predict the class or category of new instances based on these learned patterns.
Concept learning can be influenced by "concept time," which refers to the time it takes for the algorithm to learn and generalize from the training data. The complexity of the concept space and the amount of training data can impact the time required for the learning process to converge on an effective model.
In summary, concept learning begins with the concept space to derive general rules or patterns from the training data. Training data and concept time are essential factors that influence the effectiveness and efficiency of the concept learning process.
