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'''归纳逻辑程序设计 Inductive logic programming'''在生物信息学和'''自然语言处理 Natural language processing'''中特别有用。戈登 · 普洛特金 Gordon Plotkin和埃胡德 · 夏皮罗 Ehud Shapiro为归纳机器学习在逻辑上奠定了最初的理论基础。夏皮罗 Shapiro在1981年实现了他们的第一个模型推理系统: 一个从正反例中归纳推断逻辑程序的 Prolog 程序。这里的”归纳“指的是哲学上的归纳，通过提出一个理论来解释观察到的事实，而不是数学归纳法证明了一个有序集合的所有成员的性质。
'''归纳逻辑程序设计 Inductive logic programming'''在生物信息学和'''自然语言处理 Natural language processing'''中特别有用。戈登 · 普洛特金 Gordon Plotkin和埃胡德 · 夏皮罗 Ehud Shapiro为归纳机器学习在逻辑上奠定了最初的理论基础。夏皮罗 Shapiro在1981年实现了他们的第一个模型推理系统: 一个从正反例中归纳推断逻辑程序的 Prolog 程序。这里的”归纳“指的是哲学上的归纳，通过提出一个理论来解释观察到的事实，而不是数学归纳法证明了一个有序集合的所有成员的性质。
=== Models ===
=== 模型 Models ===
Performing machine learning involves creating a [[Statistical model|model]], which is trained on some training data and then can process additional data to make predictions. Various types of models have been used and researched for machine learning systems.
Performing machine learning involves creating a [[Statistical model|model]], which is trained on some training data and then can process additional data to make predictions. Various types of models have been used and researched for machine learning systems.
Performing machine learning involves creating a model, which is trained on some training data and then can process additional data to make predictions. Various types of models have been used and researched for machine learning systems.
Performing machine learning involves creating a model, which is trained on some training data and then can process additional data to make predictions. Various types of models have been used and researched for machine learning systems.
执行机器学习需要建立一个算法模型，该模型根据一些训练数据进行训练，然后可以处理额外的数据进行预测。机器学习系统已经使用和研究了各种类型的模型。
==== Artificial neural networks ====
==== 人工神经网络 Artificial neural networks ====
{{Main|Artificial neural network}}{{See also|Deep learning}}
{{Main|Artificial neural network}}{{See also|Deep learning}}
An artificial neural network is an interconnected group of nodes, akin to the vast network of [[neurons in a brain. Here, each circular node represents an artificial neuron and an arrow represents a connection from the output of one artificial neuron to the input of another.]]
An artificial neural network is an interconnected group of nodes, akin to the vast network of [[neurons in a brain. Here, each circular node represents an artificial neuron and an arrow represents a connection from the output of one artificial neuron to the input of another.]]
'''人工神经网络 Artificial neural network，ANN'''是一组相互连接的节点，类似于大脑中庞大的神经元网络。在这里，每个圆形节点代表一个人工'''神经元 Neuron'''，一个箭头代表从一个人工神经元的输出到另一个输入的连接
Artificial neural networks (ANNs), or [[Connectionism|connectionist]] systems, are computing systems vaguely inspired by the [[biological neural network]]s that constitute animal [[brain]]s. Such systems "learn" to perform tasks by considering examples, generally without being programmed with any task-specific rules.
Artificial neural networks (ANNs), or [[Connectionism|connectionist]] systems, are computing systems vaguely inspired by the [[biological neural network]]s that constitute animal [[brain]]s. Such systems "learn" to perform tasks by considering examples, generally without being programmed with any task-specific rules.
Artificial neural networks (ANNs), or connectionist systems, are computing systems vaguely inspired by the biological neural networks that constitute animal brains. Such systems "learn" to perform tasks by considering examples, generally without being programmed with any task-specific rules.
Artificial neural networks (ANNs), or connectionist systems, are computing systems vaguely inspired by the biological neural networks that constitute animal brains. Such systems "learn" to perform tasks by considering examples, generally without being programmed with any task-specific rules.
人工神经网络，或'''连接主义系统 Connectionism system'''，是计算机系统受到构成动物大脑的生物神经网络的启发后的研究成果。这种系统通过研究样本来“学习”如何执行任务，通常不需要对任何特定任务的规则进行编程。
The original goal of the ANN approach was to solve problems in the same way that a human brain would. However, over time, attention moved to performing specific tasks, leading to deviations from biology. Artificial neural networks have been used on a variety of tasks, including computer vision, speech recognition, machine translation, social network filtering, playing board and video games and medical diagnosis.
The original goal of the ANN approach was to solve problems in the same way that a human brain would. However, over time, attention moved to performing specific tasks, leading to deviations from biology. Artificial neural networks have been used on a variety of tasks, including computer vision, speech recognition, machine translation, social network filtering, playing board and video games and medical diagnosis.
人工神经网络方法的最初目标是用人类大脑解决问题的同样方式。然而，随着时间的推移，其注意力转移到执行特定的任务上，导致了与生物学的偏差。现在人工神经网络已被用于各种任务中，包括'''计算机视觉 Computer visio'''、'''语音识别 Speech recognition'''、'''机器翻译 Machine translation'''、'''社会网络过滤 Social network filtering'''、玩棋盘和视频游戏以及医疗诊断。
Deep learning consists of multiple hidden layers in an artificial neural network. This approach tries to model the way the human brain processes light and sound into vision and hearing. Some successful applications of deep learning are computer vision and speech recognition.
Deep learning consists of multiple hidden layers in an artificial neural network. This approach tries to model the way the human brain processes light and sound into vision and hearing. Some successful applications of deep learning are computer vision and speech recognition.
深度学习由人工神经网络中的多个隐层组成，通过这种方法可以尽量模拟人类大脑将光和声音处理成视觉和听觉的方式。深度学习的一些成功应用是计算机视觉和语音识别。
==== Decision trees ====
==== 决策树 Decision trees ====
{{Main|Decision tree learning}}
{{Main|Decision tree learning}}
Decision tree learning uses a decision tree as a predictive model to go from observations about an item (represented in the branches) to conclusions about the item's target value (represented in the leaves). It is one of the predictive modeling approaches used in statistics, data mining and machine learning. Tree models where the target variable can take a discrete set of values are called classification trees; in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. Decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. In decision analysis, a decision tree can be used to visually and explicitly represent decisions and decision making. In data mining, a decision tree describes data, but the resulting classification tree can be an input for decision making.
Decision tree learning uses a decision tree as a predictive model to go from observations about an item (represented in the branches) to conclusions about the item's target value (represented in the leaves). It is one of the predictive modeling approaches used in statistics, data mining and machine learning. Tree models where the target variable can take a discrete set of values are called classification trees; in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. Decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. In decision analysis, a decision tree can be used to visually and explicitly represent decisions and decision making. In data mining, a decision tree describes data, but the resulting classification tree can be an input for decision making.
'''决策树 Decision tree'''学习是使用一个决策树作为一个预测模型，从对一个项目的观察（在分支中表示）到对该项目的目标值的结论（在叶子结点中表示）。它是统计学、数据挖掘和机器学习中常用的预测建模方法之一。目标变量接受到的一组离散值的树模型称为分类树; 在这些树结构中，叶子代表类标签，分支代表连接到这些类标签的特征。其中目标变量可以取连续值（通常是实数）的决策树称为回归树。在决策分析中，可以使用决策树直观地表示决策和决策。在数据挖掘中，决策树是用来描述数据的，但得到的分类树可以作为决策的输入。
==== Support vector machines ====
==== 支持向量机 Support vector machines ====
{{Main|Support vector machines}}
{{Main|Support vector machines}}
Support vector machines (SVMs), also known as support vector networks, are a set of related supervised learning methods used for classification and regression. Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that predicts whether a new example falls into one category or the other. An SVM training algorithm is a non-probabilistic, binary, linear classifier, although methods such as Platt scaling exist to use SVM in a probabilistic classification setting. In addition to performing linear classification, SVMs can efficiently perform a non-linear classification using what is called the kernel trick, implicitly mapping their inputs into high-dimensional feature spaces.
Support vector machines (SVMs), also known as support vector networks, are a set of related supervised learning methods used for classification and regression. Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that predicts whether a new example falls into one category or the other. An SVM training algorithm is a non-probabilistic, binary, linear classifier, although methods such as Platt scaling exist to use SVM in a probabilistic classification setting. In addition to performing linear classification, SVMs can efficiently perform a non-linear classification using what is called the kernel trick, implicitly mapping their inputs into high-dimensional feature spaces.
'''支持向量机 Support vector machines，SVMs'''，也称为支持向量网络，是一系列用于分类和回归的相关监督式学习方法。给定一组训练样本，每个样本标记为两个类别中的一个，SVM 训练算法通过建立一个模型来预测一个新样本是两个类别中的哪一个。支持向量机的训练算法用到的是一种非概率的二进制线性分类器，尽管在概率分类环境中也存在使用支持向量机的方法，如 Platt 缩放法。除了执行线性分类，支持向量机可以有效地执行非线性分类使用所谓的'''核技巧 Kernel trick'''，隐式地将模型输入映射到高维特征空间。
==== Regression analysis ====
==== 回归分析 Regression analysis ====
{{Main|Regression analysis}}
{{Main|Regression analysis}}
Regression analysis encompasses a large variety of statistical methods to estimate the relationship between input variables and their associated features. Its most common form is linear regression, where a single line is drawn to best fit the given data according to a mathematical criterion such as ordinary least squares. The latter is often extended by regularization (mathematics) methods to mitigate overfitting and bias, as in ridge regression. When dealing with non-linear problems, go-to models include polynomial regression (for example, used for trendline fitting in Microsoft Excel ), Logistic regression (often used in statistical classification) or even kernel regression, which introduces non-linearity by taking advantage of the kernel trick to implicitly map input variables to higher dimensional space.
Regression analysis encompasses a large variety of statistical methods to estimate the relationship between input variables and their associated features. Its most common form is linear regression, where a single line is drawn to best fit the given data according to a mathematical criterion such as ordinary least squares. The latter is often extended by regularization (mathematics) methods to mitigate overfitting and bias, as in ridge regression. When dealing with non-linear problems, go-to models include polynomial regression (for example, used for trendline fitting in Microsoft Excel ), Logistic regression (often used in statistical classification) or even kernel regression, which introduces non-linearity by taking advantage of the kernel trick to implicitly map input variables to higher dimensional space.
'''回归分析 Regression analysis'''包含了大量的统计方法来估计输入变量和它们的相关特征之间的关系。它最常见的形式是'''线性回归 Linear regression'''，根据一个数学标准，比如一般最小平方法，画一条线来最好地拟合给定的数据。后者通常通过正则化(数学)方法来扩展，以减少过拟合和偏差，如岭回归。在处理非线性问题时，常用的模型包括多项式回归(例如，在 Microsoft Excel 中用于趋势线拟合)、 Logit模型回归（通常用于分类）甚至核回归，它利用核技巧将输入变量隐式地映射到更高维度空间，从而引入了非线性。
==== Bayesian networks ====
==== 贝叶斯网络 Bayesian networks ====
{{Main|Bayesian network}}
{{Main|Bayesian network}}
[[Image:SimpleBayesNetNodes.svg|thumb|right|A simple Bayesian network. Rain influences whether the sprinkler is activated, and both rain and the sprinkler influence whether the grass is wet.]]
[[Image:SimpleBayesNetNodes.svg|thumb|right|A simple Bayesian network. Rain influences whether the sprinkler is activated, and both rain and the sprinkler influence whether the grass is wet. 一个简单的贝叶斯网路。雨水会影响喷头是否被激活，雨水和喷头都会影响草地是否湿润。]]
A simple Bayesian network. Rain influences whether the sprinkler is activated, and both rain and the sprinkler influence whether the grass is wet.
A simple Bayesian network. Rain influences whether the sprinkler is activated, and both rain and the sprinkler influence whether the grass is wet.
一个简单的贝叶斯网路。雨水会影响喷头是否被激活，雨水和喷头都会影响草地是否湿润。
A Bayesian network, belief network or directed acyclic graphical model is a probabilistic graphical model that represents a set of random variables and their conditional independence with a directed acyclic graph (DAG). For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases. Efficient algorithms exist that perform inference and learning. Bayesian networks that model sequences of variables, like speech signals or protein sequences, are called dynamic Bayesian networks. Generalizations of Bayesian networks that can represent and solve decision problems under uncertainty are called influence diagrams.
A Bayesian network, belief network or directed acyclic graphical model is a probabilistic graphical model that represents a set of random variables and their conditional independence with a directed acyclic graph (DAG). For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases. Efficient algorithms exist that perform inference and learning. Bayesian networks that model sequences of variables, like speech signals or protein sequences, are called dynamic Bayesian networks. Generalizations of Bayesian networks that can represent and solve decision problems under uncertainty are called influence diagrams.
一个贝叶斯网路、'''信念网络 Belief network'''或'''有向无环图 Directed acyclic graph，DAG'''模型是一个概率图模型，代表一组随机变量及其条件独立与有向无环图。例如，贝叶斯网路可以表示疾病和症状之间的概率关系。在给定症状的情况下，该网络可用于计算各种疾病出现的概率。现有的高效算法可以执行推理和学习。贝叶斯网络模型的变量序列，如语音信号或蛋白质序列，被称为动态贝叶斯网络。而贝叶斯网络能够表示和解决不确定性决策问题的推广称为影响图。
==== Genetic algorithms ====
==== 遗传算法 Genetic algorithms ====
{{Main|Genetic algorithm}}
{{Main|Genetic algorithm}}
A genetic algorithm (GA) is a search algorithm and heuristic technique that mimics the process of natural selection, using methods such as mutation and crossover to generate new genotypes in the hope of finding good solutions to a given problem. In machine learning, genetic algorithms were used in the 1980s and 1990s. Conversely, machine learning techniques have been used to improve the performance of genetic and evolutionary algorithms.
A genetic algorithm (GA) is a search algorithm and heuristic technique that mimics the process of natural selection, using methods such as mutation and crossover to generate new genotypes in the hope of finding good solutions to a given problem. In machine learning, genetic algorithms were used in the 1980s and 1990s. Conversely, machine learning techniques have been used to improve the performance of genetic and evolutionary algorithms.
遗传算法(GA)是一种模仿自然选择过程的搜索算法和启发式技术，利用变异和交叉等方法产生新的基因型，以期为给定的问题找到好的解。在机器学习中，遗传算法在20世纪80年代和90年代被使用。相反，机器学习技术已被用来改善遗传和进化算法的性能。
'''遗传算法 Genetic algorithm，GA'''是一种模仿自然选择过程的搜索算法和启发式技术，利用变异和交叉等方法产生新的基因型，以期为给定的问题找到最优解。在机器学习中，遗传算法在20世纪80年代和90年代被广泛使用，而现在的机器学习技术已经可以被用来改善遗传和进化算法的性能。
=== Training models ===
=== Training models ===