その Deltaルール, also known as the Widrow-Hoffルール, is a fundamental principle in the 人工知能の分野 and ニューラルネットワーク. It is a method used to minimize the error between the predicted output and the actual target output during the training of a model. The Delta Rule is particularly important in 教師あり学習, where a model learns from labeled input data.
要するに、デルタルールは weights of a ニューラルネットワーク by calculating the difference, or ‘delta’, between the expected output (target value) and the actual output produced by the network. This error is then used to adjust the weights of the connections in the network to improve accuracy. Mathematically, the weight update can be expressed as:
wnew = wold + η * δ * x
ただし：
wnewis the updated weight.woldis the current weight.η(eta) is the 学習率, which determines how much the weights should be adjusted.δis the error term, calculated as the difference between the actual output and the target output.xis the input value associated with the weight being updated.
The Delta Rule emphasizes the importance of adjusting weights in the direction that reduces the error, thereby improving the model’s performance over time. This process is repeated iteratively across many training samples, allowing the neural network to learn complex パターンを学習し、正確な予測を行う。