学習ベクトル 量子化 (LVQ) is a type of 教師あり学習 algorithm that is primarily used for classification tasks within the field of 機械学習. The core idea behind LVQ is to classify data points by associating them with prototypes, which are representative vectors in the 特徴空間. These prototypes are typically derived from the 訓練データ 訓練過程で調整され、分類誤差を最小限に抑えるために使用される。
In LVQ, each class in the dataset is represented by a defined number of prototypes. When a new data point is introduced, the algorithm identifies the closest prototype in the feature space, using a distance metric such as ユークリッド距離. The class associated with the nearest prototype is then assigned to the new data point. This process allows LVQ to effectively capture the underlying patterns of the data while maintaining a relatively simple model.
The training phase of LVQ involves iteratively adjusting the prototypes based on the classification results. When a prototype correctly classifies a data point, it may be moved slightly closer to that data point. Conversely, if a prototype incorrectly classifies a data point, it is adjusted away from the misclassified point. This adjustment mechanism helps the algorithm improve its accuracy データの総量を表します。
LVQは、特徴空間でクラスが十分に分離されているシナリオで特に有用です。ただし、重なり合うクラスや高次元のデータには苦労することがあります。Soft LVQやGeneralized LVQなどのLVQのバリアントは、これらの制限の一部に対処し、さまざまな応用での性能を向上させるために開発されています。