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Supervised learning algorithms build a mathematical model of a set of data that contains both the inputs and the desired outputs. 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. 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.

Supervised learning algorithms build a mathematical model of a set of data that contains both the inputs and the desired outputs. 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. 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.

有监督学习算法会建立一个包含输入和期望输出的数据集上的的数学模型。这些数据被称为训练数据，由一组组训练样本组成。每个训练样本都有一个或多个输入和期望的输出，也称为监督信号。在数学模型中，每个训练样本由一个数组或向量表示，有时也称为'''特征向量 Feature vector'''，训练数据由一个矩阵表示。通过对目标函数的迭代优化，监督式学习算法可以学习到一个用来预测与新输入相关的输出的函数。一个达到最优的目标函数可以实现算法对未知输入的输出结果有正确的预判，这种正确的预判并不仅限于训练数据上（即模型具有良好的泛化能力）。随着时间的推移，提高输出或预测精度的算法被称为已学会执行该任务。

有监督学习算法会建立一个包含输入和期望输出的数据集上的的数学模型。这些数据被称为训练数据，由一组组训练样本组成。每个训练样本都有一个或多个输入和期望的输出，也称为监督信号。在数学模型中，每个训练样本由一个数组或向量表示，有时也称为'''特征向量 Feature Vector'''，训练数据由一个矩阵表示。通过对目标函数的迭代优化，监督式学习算法可以学习到一个用来预测与新输入相关的输出的函数。一个达到最优的目标函数可以实现算法对未知输入的输出结果有正确的预判，这种正确的预判并不仅限于训练数据上（即模型具有良好的泛化能力）。随着时间的推移，提高输出或预测精度的算法被称为已学会执行该任务。