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Mitchell provided a widely quoted, more formal definition of the algorithms studied in the machine learning field: Machine learning tasks are classified into several broad categories.

In supervised learning , the algorithm builds a mathematical model of a set of data that contains both the inputs and the desired outputs. For example, if the task were determining whether an image contained a certain object, the training data for a supervised learning algorithm would include images with and without that object the input , and each image would have a label the output designating whether it contained the object.

In special cases, the input may be only partially available, or restricted to special feedback. Classification algorithms and regression algorithms are types of supervised learning.

Classification algorithms are used when the outputs are restricted to a limited set of values. For a classification algorithm that filters emails, the input would be an incoming email, and the output would be the name of the folder in which to file the email.

For an algorithm that identifies spam emails, the output would be the prediction of either "spam" or "not spam", represented by the Boolean values true and false.

Regression algorithms are named for their continuous outputs, meaning they may have any value within a range. Examples of a continuous value are the temperature, length, or price of an object.

In unsupervised learning , the algorithm builds a mathematical model of a set of data which contains only inputs and no desired outputs.

Unsupervised learning algorithms are used to find structure in the data, like grouping or clustering of data points.

Unsupervised learning can discover patterns in the data, and can group the inputs into categories, as in feature learning. Dimensionality reduction is the process of reducing the number of "features", or inputs, in a set of data.

Active learning algorithms access the desired outputs training labels for a limited set of inputs based on a budget, and optimize the choice of inputs for which it will acquire training labels.

When used interactively, these can be presented to a human user for labeling. Reinforcement learning algorithms are given feedback in the form of positive or negative reinforcement in a dynamic environment, and are used in autonomous vehicles or in learning to play a game against a human opponent.

Machine learning algorithms can be used to find the unobservable probability density function in density estimation problems. Meta learning algorithms learn their own inductive bias based on previous experience.

In developmental robotics , robot learning algorithms generate their own sequences of learning experiences, also known as a curriculum, to cumulatively acquire new skills through self-guided exploration and social interaction with humans.

These robots use guidance mechanisms such as active learning, maturation, motor synergies, and imitation. Arthur Samuel , an American pioneer in the field of computer gaming and artificial intelligence , coined the term "Machine Learning" in while at IBM [8].

As a scientific endeavour, machine learning grew out of the quest for artificial intelligence. Already in the early days of AI as an academic discipline, some researchers were interested in having machines learn from data.

They attempted to approach the problem with various symbolic methods, as well as what were then termed " neural networks "; these were mostly perceptrons and other models that were later found to be reinventions of the generalized linear models of statistics.

However, an increasing emphasis on the logical, knowledge-based approach caused a rift between AI and machine learning.

Probabilistic systems were plagued by theoretical and practical problems of data acquisition and representation. Their main success came in the mids with the reinvention of backpropagation.

Machine learning, reorganized as a separate field, started to flourish in the s. The field changed its goal from achieving artificial intelligence to tackling solvable problems of a practical nature.

It shifted focus away from the symbolic approaches it had inherited from AI, and toward methods and models borrowed from statistics and probability theory.

Machine learning and data mining often employ the same methods and overlap significantly, but while machine learning focuses on prediction, based on known properties learned from the training data, data mining focuses on the discovery of previously unknown properties in the data this is the analysis step of knowledge discovery in databases.

Data mining uses many machine learning methods, but with different goals; on the other hand, machine learning also employs data mining methods as "unsupervised learning" or as a preprocessing step to improve learner accuracy.

Much of the confusion between these two research communities which do often have separate conferences and separate journals, ECML PKDD being a major exception comes from the basic assumptions they work with: Evaluated with respect to known knowledge, an uninformed unsupervised method will easily be outperformed by other supervised methods, while in a typical KDD task, supervised methods cannot be used due to the unavailability of training data.

Machine learning also has intimate ties to optimization: Loss functions express the discrepancy between the predictions of the model being trained and the actual problem instances for example, in classification, one wants to assign a label to instances, and models are trained to correctly predict the pre-assigned labels of a set of examples.

The difference between the two fields arises from the goal of generalization: Machine learning and statistics are closely related fields.

According to Michael I. Jordan , the ideas of machine learning, from methodological principles to theoretical tools, have had a long pre-history in statistics.

Leo Breiman distinguished two statistical modelling paradigms: Some statisticians have adopted methods from machine learning, leading to a combined field that they call statistical learning.

A core objective of a learner is to generalize from its experience. The training examples come from some generally unknown probability distribution considered representative of the space of occurrences and the learner has to build a general model about this space that enables it to produce sufficiently accurate predictions in new cases.

The computational analysis of machine learning algorithms and their performance is a branch of theoretical computer science known as computational learning theory.

Because training sets are finite and the future is uncertain, learning theory usually does not yield guarantees of the performance of algorithms.

Instead, probabilistic bounds on the performance are quite common. The bias—variance decomposition is one way to quantify generalization error.

For the best performance in the context of generalization, the complexity of the hypothesis should match the complexity of the function underlying the data.

If the hypothesis is less complex than the function, then the model has underfit the data. If the complexity of the model is increased in response, then the training error decreases.

But if the hypothesis is too complex, then the model is subject to overfitting and generalization will be poorer. In addition to performance bounds, computational learning theorists study the time complexity and feasibility of learning.

In computational learning theory, a computation is considered feasible if it can be done in polynomial time. There are two kinds of time complexity results.

Positive results show that a certain class of functions can be learned in polynomial time. Negative results show that certain classes cannot be learned in polynomial time.

The types of machine learning algorithms differ in their approach, the type of data they input and output, and the type of task or problem that they are intended to solve.

Supervised learning algorithms build a mathematical model of a set of data that contains both the inputs and the desired outputs. Each training example has one or more inputs and a desired output, also known as a supervisory signal.

In the case of semi-supervised learning algorithms, some of the training examples are missing the desired output. In the mathematical model, each training example is represented by an array or vector, and the training data 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 algorithm that improves the accuracy of its outputs or predictions over time is said to have learned to perform that task. Supervised learning algorithms include classification and regression.

Similarity learning is an area of supervised machine learning closely related to regression and classification, but the goal is to learn from examples using a similarity function that measures how similar or related two objects are.

It has applications in ranking , recommendation systems , visual identity tracking, face verification, and speaker verification.

Unsupervised learning algorithms take a set of data that contains only inputs, and find structure in the data, like grouping or clustering of data points.

The algorithms therefore learn from test data that has not been labeled, classified or categorized. Instead of responding to feedback, unsupervised learning algorithms identify commonalities in the data and react based on the presence or absence of such commonalities in each new piece of data.

A central application of unsupervised learning is in the field of density estimation in statistics , [21] though unsupervised learning encompasses other domains involving summarizing and explaining data features.

Cluster analysis is the assignment of a set of observations into subsets called clusters so that observations within the same cluster are similar according to one or more predesignated criteria, while observations drawn from different clusters are dissimilar.

Different clustering techniques make different assumptions on the structure of the data, often defined by some similarity metric and evaluated, for example, by internal compactness , or the similarity between members of the same cluster, and separation , the difference between clusters.

Other methods are based on estimated density and graph connectivity. Reinforcement learning is an area of machine learning concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward.

Due to its generality, the field is studied in many other disciplines, such as game theory , control theory , operations research , information theory , simulation-based optimization , multi-agent systems , swarm intelligence , statistics and genetic algorithms.

Many reinforcement learning algorithms use dynamic programming techniques. Various processes, techniques and methods can be applied to one or more types of machine learning algorithms to enhance their performance.

Several learning algorithms aim at discovering better representations of the inputs provided during training. Feature learning algorithms, also called representation learning algorithms, often attempt to preserve the information in their input but also transform it in a way that makes it useful, often as a pre-processing step before performing classification or predictions.

This technique allows reconstruction of the inputs coming from the unknown data-generating distribution, while not being necessarily faithful to configurations that are implausible under that distribution.

This replaces manual feature engineering , and allows a machine to both learn the features and use them to perform a specific task.

Feature learning can be either supervised or unsupervised. In supervised feature learning, features are learned using labeled input data.

Examples include artificial neural networks , multilayer perceptrons , and supervised dictionary learning.

In unsupervised feature learning, features are learned with unlabeled input data. Examples include dictionary learning, independent component analysis , autoencoders , matrix factorization [26] and various forms of clustering.

Manifold learning algorithms attempt to do so under the constraint that the learned representation is low-dimensional. Sparse coding algorithms attempt to do so under the constraint that the learned representation is sparse, meaning that the mathematical model has many zeros.

Multilinear subspace learning algorithms aim to learn low-dimensional representations directly from tensor representations for multidimensional data, without reshaping them into higher-dimensional vectors.

It has been argued that an intelligent machine is one that learns a representation that disentangles the underlying factors of variation that explain the observed data.

Feature learning is motivated by the fact that machine learning tasks such as classification often require input that is mathematically and computationally convenient to process.

However, real-world data such as images, video, and sensory data has not yielded to attempts to algorithmically define specific features.

An alternative is to discover such features or representations through examination, without relying on explicit algorithms. Sparse dictionary learning is a feature learning method where a training example is represented as a linear combination of basis functions , and is assumed to be a sparse matrix.

The method is strongly NP-hard and difficult to solve approximately. Sparse dictionary learning has been applied in several contexts.

In classification, the problem is to determine to which classes a previously unseen training example belongs. For a dictionary where each class has already been built, a new training example is associated with the class that is best sparsely represented by the corresponding dictionary.

Sparse dictionary learning has also been applied in image de-noising. The key idea is that a clean image patch can be sparsely represented by an image dictionary, but the noise cannot.

In data mining , anomaly detection, also known as outlier detection, is the identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data.

Anomalies are referred to as outliers , novelties, noise, deviations and exceptions. In particular, in the context of abuse and network intrusion detection, the interesting objects are often not rare objects, but unexpected bursts in activity.

This pattern does not adhere to the common statistical definition of an outlier as a rare object, and many outlier detection methods in particular, unsupervised algorithms will fail on such data, unless it has been aggregated appropriately.

Instead, a cluster analysis algorithm may be able to detect the micro-clusters formed by these patterns. Three broad categories of anomaly detection techniques exist.

Supervised anomaly detection techniques require a data set that has been labeled as "normal" and "abnormal" and involves training a classifier the key difference to many other statistical classification problems is the inherent unbalanced nature of outlier detection.

Semi-supervised anomaly detection techniques construct a model representing normal behavior from a given normal training data set, and then test the likelihood of a test instance to be generated by the model.

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. Association rule learning is a rule-based machine learning method for discovering relationships between variables in large databases.

It is intended to identify strong rules discovered in databases using some measure of "interestingness". Rule-based machine learning is a general term for any machine learning method that identifies, learns, or evolves "rules" to store, manipulate or apply knowledge.

The defining characteristic of a rule-based machine learning algorithm is the identification and utilization of a set of relational rules that collectively represent the knowledge captured by the system.

This is in contrast to other machine learning algorithms that commonly identify a singular model that can be universally applied to any instance in order to make a prediction.

Such information can be used as the basis for decisions about marketing activities such as promotional pricing or product placements. In addition to market basket analysis , association rules are employed today in application areas including Web usage mining , intrusion detection , continuous production , and bioinformatics.

In contrast with sequence mining , association rule learning typically does not consider the order of items either within a transaction or across transactions.

Learning classifier systems LCS are a family of rule-based machine learning algorithms that combine a discovery component, typically a genetic algorithm , with a learning component, performing either supervised learning , reinforcement learning , or unsupervised learning.

They seek to identify a set of context-dependent rules that collectively store and apply knowledge in a piecewise manner in order to make predictions.

Inductive logic programming ILP is an approach to rule-learning using logic programming as a uniform representation for input examples, background knowledge, and hypotheses.

Given an encoding of the known background knowledge and a set of examples represented as a logical database of facts, an ILP system will derive a hypothesized logic program that entails all positive and no negative examples.

Inductive programming is a related field that considers any kind of programming languages for representing hypotheses and not only logic programming , such as functional programs.

Inductive logic programming is particularly useful in bioinformatics and natural language processing. Gordon Plotkin and Ehud Shapiro laid the initial theoretical foundation for inductive machine learning in a logical setting.

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ADCO stores and processes personalized information of the user and this is only retrieved once it specifically relates to the process or for statistical purposes.

User data are strictly confidential and handled in this way, and are never sold, leased or disclosed to a third party. For statistical purposes specific Access data and profile information concerning the use of this website are saved always anonymously.

They are exclusively employed to improve visitor guidance on the website and to optimize the offering. Personal data are only stored for as long as the user permits and by means of the authority he bestows upon ADCO with respects to the German Federal Data Protection Act.

By using the communication offering of the ADCO website, the user acknowledges that this site is primarily meant to promote contact with potential new clients by arousing user interest in the services offered by ADCO.

Before the transmission of data to ADCO, the user has to grant permission that they are in full agreement that the data can be used as described.

Arthur Samuel , an American pioneer in the field of computer gaming and artificial intelligence , coined the term "Machine Learning" in while at IBM [8].

As a scientific endeavour, machine learning grew out of the quest for artificial intelligence. Already in the early days of AI as an academic discipline, some researchers were interested in having machines learn from data.

They attempted to approach the problem with various symbolic methods, as well as what were then termed " neural networks "; these were mostly perceptrons and other models that were later found to be reinventions of the generalized linear models of statistics.

However, an increasing emphasis on the logical, knowledge-based approach caused a rift between AI and machine learning. Probabilistic systems were plagued by theoretical and practical problems of data acquisition and representation.

Their main success came in the mids with the reinvention of backpropagation. Machine learning, reorganized as a separate field, started to flourish in the s.

The field changed its goal from achieving artificial intelligence to tackling solvable problems of a practical nature.

It shifted focus away from the symbolic approaches it had inherited from AI, and toward methods and models borrowed from statistics and probability theory.

Machine learning and data mining often employ the same methods and overlap significantly, but while machine learning focuses on prediction, based on known properties learned from the training data, data mining focuses on the discovery of previously unknown properties in the data this is the analysis step of knowledge discovery in databases.

Data mining uses many machine learning methods, but with different goals; on the other hand, machine learning also employs data mining methods as "unsupervised learning" or as a preprocessing step to improve learner accuracy.

Much of the confusion between these two research communities which do often have separate conferences and separate journals, ECML PKDD being a major exception comes from the basic assumptions they work with: Evaluated with respect to known knowledge, an uninformed unsupervised method will easily be outperformed by other supervised methods, while in a typical KDD task, supervised methods cannot be used due to the unavailability of training data.

Machine learning also has intimate ties to optimization: Loss functions express the discrepancy between the predictions of the model being trained and the actual problem instances for example, in classification, one wants to assign a label to instances, and models are trained to correctly predict the pre-assigned labels of a set of examples.

The difference between the two fields arises from the goal of generalization: Machine learning and statistics are closely related fields.

According to Michael I. Jordan , the ideas of machine learning, from methodological principles to theoretical tools, have had a long pre-history in statistics.

Leo Breiman distinguished two statistical modelling paradigms: Some statisticians have adopted methods from machine learning, leading to a combined field that they call statistical learning.

A core objective of a learner is to generalize from its experience. The training examples come from some generally unknown probability distribution considered representative of the space of occurrences and the learner has to build a general model about this space that enables it to produce sufficiently accurate predictions in new cases.

The computational analysis of machine learning algorithms and their performance is a branch of theoretical computer science known as computational learning theory.

Because training sets are finite and the future is uncertain, learning theory usually does not yield guarantees of the performance of algorithms.

Instead, probabilistic bounds on the performance are quite common. The bias—variance decomposition is one way to quantify generalization error.

For the best performance in the context of generalization, the complexity of the hypothesis should match the complexity of the function underlying the data.

If the hypothesis is less complex than the function, then the model has underfit the data. If the complexity of the model is increased in response, then the training error decreases.

But if the hypothesis is too complex, then the model is subject to overfitting and generalization will be poorer. In addition to performance bounds, computational learning theorists study the time complexity and feasibility of learning.

In computational learning theory, a computation is considered feasible if it can be done in polynomial time.

There are two kinds of time complexity results. Positive results show that a certain class of functions can be learned in polynomial time.

Negative results show that certain classes cannot be learned in polynomial time. The types of machine learning algorithms differ in their approach, the type of data they input and output, and the type of task or problem that they are intended to solve.

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

Each training example has one or more inputs and a desired output, also known as a supervisory signal. In the case of semi-supervised learning algorithms, some of the training examples are missing the desired output.

In the mathematical model, each training example is represented by an array or vector, and the training data 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 algorithm that improves the accuracy of its outputs or predictions over time is said to have learned to perform that task. Supervised learning algorithms include classification and regression.

Similarity learning is an area of supervised machine learning closely related to regression and classification, but the goal is to learn from examples using a similarity function that measures how similar or related two objects are.

It has applications in ranking , recommendation systems , visual identity tracking, face verification, and speaker verification.

Unsupervised learning algorithms take a set of data that contains only inputs, and find structure in the data, like grouping or clustering of data points.

The algorithms therefore learn from test data that has not been labeled, classified or categorized. Instead of responding to feedback, unsupervised learning algorithms identify commonalities in the data and react based on the presence or absence of such commonalities in each new piece of data.

A central application of unsupervised learning is in the field of density estimation in statistics , [21] though unsupervised learning encompasses other domains involving summarizing and explaining data features.

Cluster analysis is the assignment of a set of observations into subsets called clusters so that observations within the same cluster are similar according to one or more predesignated criteria, while observations drawn from different clusters are dissimilar.

Different clustering techniques make different assumptions on the structure of the data, often defined by some similarity metric and evaluated, for example, by internal compactness , or the similarity between members of the same cluster, and separation , the difference between clusters.

Other methods are based on estimated density and graph connectivity. Reinforcement learning is an area of machine learning concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward.

Due to its generality, the field is studied in many other disciplines, such as game theory , control theory , operations research , information theory , simulation-based optimization , multi-agent systems , swarm intelligence , statistics and genetic algorithms.

Many reinforcement learning algorithms use dynamic programming techniques. Various processes, techniques and methods can be applied to one or more types of machine learning algorithms to enhance their performance.

Several learning algorithms aim at discovering better representations of the inputs provided during training. Feature learning algorithms, also called representation learning algorithms, often attempt to preserve the information in their input but also transform it in a way that makes it useful, often as a pre-processing step before performing classification or predictions.

This technique allows reconstruction of the inputs coming from the unknown data-generating distribution, while not being necessarily faithful to configurations that are implausible under that distribution.

This replaces manual feature engineering , and allows a machine to both learn the features and use them to perform a specific task.

Feature learning can be either supervised or unsupervised. In supervised feature learning, features are learned using labeled input data.

Examples include artificial neural networks , multilayer perceptrons , and supervised dictionary learning. In unsupervised feature learning, features are learned with unlabeled input data.

Examples include dictionary learning, independent component analysis , autoencoders , matrix factorization [26] and various forms of clustering.

Manifold learning algorithms attempt to do so under the constraint that the learned representation is low-dimensional.

Sparse coding algorithms attempt to do so under the constraint that the learned representation is sparse, meaning that the mathematical model has many zeros.

Multilinear subspace learning algorithms aim to learn low-dimensional representations directly from tensor representations for multidimensional data, without reshaping them into higher-dimensional vectors.

It has been argued that an intelligent machine is one that learns a representation that disentangles the underlying factors of variation that explain the observed data.

Feature learning is motivated by the fact that machine learning tasks such as classification often require input that is mathematically and computationally convenient to process.

However, real-world data such as images, video, and sensory data has not yielded to attempts to algorithmically define specific features. An alternative is to discover such features or representations through examination, without relying on explicit algorithms.

Sparse dictionary learning is a feature learning method where a training example is represented as a linear combination of basis functions , and is assumed to be a sparse matrix.

The method is strongly NP-hard and difficult to solve approximately. Sparse dictionary learning has been applied in several contexts. In classification, the problem is to determine to which classes a previously unseen training example belongs.

For a dictionary where each class has already been built, a new training example is associated with the class that is best sparsely represented by the corresponding dictionary.

Sparse dictionary learning has also been applied in image de-noising. The key idea is that a clean image patch can be sparsely represented by an image dictionary, but the noise cannot.

In data mining , anomaly detection, also known as outlier detection, is the identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data.

Anomalies are referred to as outliers , novelties, noise, deviations and exceptions. In particular, in the context of abuse and network intrusion detection, the interesting objects are often not rare objects, but unexpected bursts in activity.

This pattern does not adhere to the common statistical definition of an outlier as a rare object, and many outlier detection methods in particular, unsupervised algorithms will fail on such data, unless it has been aggregated appropriately.

Instead, a cluster analysis algorithm may be able to detect the micro-clusters formed by these patterns. Three broad categories of anomaly detection techniques exist.

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