訓練、トレーニング、学習、モデル

AI・機械学習の用語辞典、＠IT

トレーニング（training、訓練）とは？
モデルのパラメーターを自動的に調整

学習（learning）とは？
「モデルを作ること。トレーニングデータを入力しながら、適切な出力を行う具体的な計算式／計算方法を求めていく（＝トレーニングしていく）作業」

モデル（model）とは？
「学習後の具体的な計算式／計算方法「学習済みモデル（Learned model、学習モデル）」「トレーニング済みモデル（Trained model、訓練済みモデル）」とも呼ばれる。」

「パラメーターが決まった具体的な式」

「［入力］→［モデル］→［出力］」

「機械学習のモデルを作成（＝学習）する手順／方法は「手法」と呼ばれる。」

 

深層学習はどのように賢くなるのか

「訓練する（学習させる）」

学習：「データから、最もうまく予言できるパラメータを決める」

「データの学習」

機械学習の種類

「相関関係を見出して、数式化する（モデルを構築する）」

機械学習、Wikipedia

機械学習（きかいがくしゅう、英: machine learning）とは、経験からの学習により自動で改善するコンピューターアルゴリズムもしくはその研究領域で[1][2]、人工知能の一種であるとみなされている。「訓練データ」もしくは「学習データ」と呼ばれるデータを使って学習し、学習結果を使って何らかのタスクをこなす。例えば過去のスパムメールを訓練データとして用いて学習し、スパムフィルタリングというタスクをこなす、といった事が可能となる。

訓練データを使ってプログラムの性能を改善する過程を、「プログラムを訓練する」もしくは「プログラムを学習させる」という。

教師あり学習では訓練データの事を教師データとも呼ぶ

Machine learning, Wikipedia

Machine learning (ML) is a field of inquiry devoted to understanding and building methods that 'learn', that is, methods that leverage data to improve performance on some set of tasks.[1] It is seen as a part of artificial intelligence. Machine learning algorithms build a model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so.[2] Machine learning algorithms are used in a wide variety of applications, such as in medicine, email filtering, speech recognition, and computer vision, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks.[3]

 

The discipline of machine learning employs various approaches to teach computers to accomplish tasks where no fully satisfactory algorithm is available. In cases where vast numbers of potential answers exist, one approach is to label some of the correct answers as valid. This can then be used as training data for the computer to improve the algorithm(s) it uses to determine correct answers. For example, to train a system for the task of digital character recognition, the MNIST dataset of handwritten digits has often been used.[10]

 

Machine learning approaches are traditionally divided into three broad categories, depending on the nature of the "signal" or "feedback" available to the learning system:

Supervised learning: The computer is presented with example inputs and their desired outputs, given by a "teacher", and the goal is to learn a general rule that maps inputs to outputs.
Unsupervised learning: No labels are given to the learning algorithm, leaving it on its own to find structure in its input. Unsupervised learning can be a goal in itself (discovering hidden patterns in data) or a means towards an end (feature learning).
Reinforcement learning: A computer program interacts with a dynamic environment in which it must perform a certain goal (such as driving a vehicle or playing a game against an opponent). As it navigates its problem space, the program is provided feedback that's analogous to rewards, which it tries to maximize.[4]

Supervised learning
Main article: Supervised learning

A support-vector machine is a supervised learning model that divides the data into regions separated by a linear boundary. Here, the linear boundary divides the black circles from the white.
Supervised learning algorithms build a mathematical model of a set of data that contains both the inputs and the desired outputs.[34] The data is known as training data, and consists of a set of training examples. Each training example has one or more inputs and the desired output, also known as a supervisory signal. In the mathematical model, each training example is represented by an array or vector, sometimes called a feature vector, and the training data is represented by a matrix. Through iterative optimization of an objective function, supervised learning algorithms learn a function that can be used to predict the output associated with new inputs.[35] An optimal function will allow the algorithm to correctly determine the output for inputs that were not a part of the training data. An algorithm that improves the accuracy of its outputs or predictions over time is said to have learned to perform that task.[18]