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https://en.wikipedia.org/wiki/Machine_learning#0 | Machine learning
Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalise to unseen data, and thus perform tasks without explicit instructions.[1] Within a subdiscipline in machine learning, advances i... |
https://en.wikipedia.org/wiki/Machine_learning#1 | rning approaches in performance.[2]
ML finds application in many fields, including natural language processing, computer vision, speech recognition, email filtering, agriculture, and medicine.[3][4] The application of ML to business problems is known as predictive analytics.
Statistics and mathematical optimisation (ma... |
https://en.wikipedia.org/wiki/Machine_learning#2 | exploratory data analysis (EDA) via unsupervised learning.[6][7]
From a theoretical viewpoint, probably approximately correct learning provides a framework for describing machine learning.
History
[edit]The term machine learning was coined in 1959 by Arthur Samuel, an IBM employee and pioneer in the field of computer g... |
https://en.wikipedia.org/wiki/Machine_learning#3 | the earliest machine learning model was introduced in the 1950s when Arthur Samuel invented a program that calculated the winning chance in checkers for each side, the history of machine learning roots back to decades of human desire and effort to study human cognitive processes.[12] In 1949, Canadian psychologist Dona... |
https://en.wikipedia.org/wiki/Machine_learning#4 | nteractions among nerve cells.[13] Hebb's model of neurons interacting with one another set a groundwork for how AIs and machine learning algorithms work under nodes, or artificial neurons used by computers to communicate data.[12] Other researchers who have studied human cognitive systems contributed to the modern mac... |
https://en.wikipedia.org/wiki/Machine_learning#5 | ls of neural networks to come up with algorithms that mirror human thought processes.[12]
By the early 1960s, an experimental "learning machine" with punched tape memory, called Cybertron, had been developed by Raytheon Company to analyse sonar signals, electrocardiograms, and speech patterns using rudimentary reinforc... |
https://en.wikipedia.org/wiki/Machine_learning#6 | to cause it to reevaluate incorrect decisions.[14] A representative book on research into machine learning during the 1960s was Nilsson's book on Learning Machines, dealing mostly with machine learning for pattern classification.[15] Interest related to pattern recognition continued into the 1970s, as described by Duda... |
https://en.wikipedia.org/wiki/Machine_learning#7 | gnise 40 characters (26 letters, 10 digits, and 4 special symbols) from a computer terminal.[17]
Tom M. Mitchell provided a widely quoted, more formal definition of the algorithms studied in the machine learning field: "A computer program is said to learn from experience E with respect to some class of tasks T and perf... |
https://en.wikipedia.org/wiki/Machine_learning#8 | s in which machine learning is concerned offers a fundamentally operational definition rather than defining the field in cognitive terms. This follows Alan Turing's proposal in his paper "Computing Machinery and Intelligence", in which the question "Can machines think?" is replaced with the question "Can machines do wh... |
https://en.wikipedia.org/wiki/Machine_learning#9 | which have been developed; the other purpose is to make predictions for future outcomes based on these models. A hypothetical algorithm specific to classifying data may use computer vision of moles coupled with supervised learning in order to train it to classify the cancerous moles. A machine learning algorithm for st... |
https://en.wikipedia.org/wiki/Machine_learning#10 | e
[edit]As a scientific endeavour, machine learning grew out of the quest for artificial intelligence (AI). 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 t... |
https://en.wikipedia.org/wiki/Machine_learning#11 | alised linear models of statistics.[22] Probabilistic reasoning was also employed, especially in automated medical diagnosis.[23]: 488
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 p... |
https://en.wikipedia.org/wiki/Machine_learning#12 | f favour.[24] Work on symbolic/knowledge-based learning did continue within AI, leading to inductive logic programming(ILP), but the more statistical line of research was now outside the field of AI proper, in pattern recognition and information retrieval.[23]: 708–710, 755 Neural networks research had been abandoned b... |
https://en.wikipedia.org/wiki/Machine_learning#13 | archers from other disciplines including John Hopfield, David Rumelhart, and Geoffrey Hinton. Their main success came in the mid-1980s with the reinvention of backpropagation.[23]: 25
Machine learning (ML), reorganised and recognised as its own field, started to flourish in the 1990s. The field changed its goal from ac... |
https://en.wikipedia.org/wiki/Machine_learning#14 | aches it had inherited from AI, and toward methods and models borrowed from statistics, fuzzy logic, and probability theory.[24]
Data compression
[edit]There is a close connection between machine learning and compression. A system that predicts the posterior probabilities of a sequence given its entire history can be u... |
https://en.wikipedia.org/wiki/Machine_learning#15 | used for prediction (by finding the symbol that compresses best, given the previous history). This equivalence has been used as a justification for using data compression as a benchmark for "general intelligence".[25][26][27]
An alternative view can show compression algorithms implicitly map strings into implicit featu... |
https://en.wikipedia.org/wiki/Machine_learning#16 | (.) we define an associated vector space ℵ, such that C(.) maps an input string x, corresponding to the vector norm ||~x||. An exhaustive examination of the feature spaces underlying all compression algorithms is precluded by space; instead, feature vectors chooses to examine three representative lossless compression m... |
https://en.wikipedia.org/wiki/Machine_learning#17 | compression of x is the smallest possible software that generates x. For example, in that model, a zip file's compressed size includes both the zip file and the unzipping software, since you can not unzip it without both, but there may be an even smaller combined form.
Examples of AI-powered audio/video compression sof... |
https://en.wikipedia.org/wiki/Machine_learning#18 | ow, MATLAB's Image Processing Toolbox (IPT) and High-Fidelity Generative Image Compression.[30]
In unsupervised machine learning, k-means clustering can be utilized to compress data by grouping similar data points into clusters. This technique simplifies handling extensive datasets that lack predefined labels and finds... |
https://en.wikipedia.org/wiki/Machine_learning#19 | efficiency and speeding up data transmission. K-means clustering, an unsupervised machine learning algorithm, is employed to partition a dataset into a specified number of clusters, k, each represented by the centroid of its points. This process condenses extensive datasets into a more compact set of representative poi... |
https://en.wikipedia.org/wiki/Machine_learning#20 | a points with their centroids, thereby preserving the core information of the original data while significantly decreasing the required storage space.[32]
Large language models (LLMs) are also efficient lossless data compressors on some data sets, as demonstrated by DeepMind's research with the Chinchilla 70B model. De... |
https://en.wikipedia.org/wiki/Machine_learning#21 | ics (PNG) for images and Free Lossless Audio Codec (FLAC) for audio. It achieved compression of image and audio data to 43.4% and 16.4% of their original sizes, respectively. There is, however, some reason to be concerned that the data set used for testing overlaps the LLM training data set, making it possible that the... |
https://en.wikipedia.org/wiki/Machine_learning#22 | ne 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 dis... |
https://en.wikipedia.org/wiki/Machine_learning#23 | g 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 wor... |
https://en.wikipedia.org/wiki/Machine_learning#24 | nowledge discovery and data mining (KDD) the key task is the discovery of previously unknown knowledge. 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 unavai... |
https://en.wikipedia.org/wiki/Machine_learning#25 | isation of some loss function on a training set of examples. 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 preassigned l... |
https://en.wikipedia.org/wiki/Machine_learning#26 | e topic of current research, especially for deep learning algorithms.
Statistics
[edit]Machine learning and statistics are closely related fields in terms of methods, but distinct in their principal goal: statistics draws population inferences from a sample, while machine learning finds generalisable predictive pattern... |
https://en.wikipedia.org/wiki/Machine_learning#27 | d a long pre-history in statistics.[37] He also suggested the term data science as a placeholder to call the overall field.[37]
Conventional statistical analyses require the a priori selection of a model most suitable for the study data set. In addition, only significant or theoretically relevant variables based on pre... |
https://en.wikipedia.org/wiki/Machine_learning#28 | shape the model by detecting underlying patterns. The more variables (input) used to train the model, the more accurate the ultimate model will be.[38]
Leo Breiman distinguished two statistical modelling paradigms: data model and algorithmic model,[39] wherein "algorithmic model" means more or less the machine learning... |
https://en.wikipedia.org/wiki/Machine_learning#29 | hey call statistical learning.[40]
Statistical physics
[edit]Analytical and computational techniques derived from deep-rooted physics of disordered systems can be extended to large-scale problems, including machine learning, e.g., to analyse the weight space of deep neural networks.[41] Statistical physics is thus find... |
https://en.wikipedia.org/wiki/Machine_learning#30 | rience.[5][43] Generalisation in this context is the ability of a learning machine to perform accurately on new, unseen examples/tasks after having experienced a learning data set. The training examples come from some generally unknown probability distribution (considered representative of the space of occurrences) and... |
https://en.wikipedia.org/wiki/Machine_learning#31 | es.
The computational analysis of machine learning algorithms and their performance is a branch of theoretical computer science known as computational learning theory via the probably approximately correct learning model. Because training sets are finite and the future is uncertain, learning theory usually does not yie... |
https://en.wikipedia.org/wiki/Machine_learning#32 | ce decomposition is one way to quantify generalisation error.
For the best performance in the context of generalisation, 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 under fitted the data. If the... |
https://en.wikipedia.org/wiki/Machine_learning#33 | he model is subject to overfitting and generalisation will be poorer.[44]
In addition to performance bounds, 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 ... |
https://en.wikipedia.org/wiki/Machine_learning#34 | ow that certain classes cannot be learned in polynomial time.
Approaches
[edit]
Machine learning approaches are traditionally divided into three broad categories, which correspond to learning paradigms, depending on the nature of the "signal" or "feedback" available to the learning system:
- Supervised learning: The co... |
https://en.wikipedia.org/wiki/Machine_learning#35 | hat 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... |
https://en.wikipedia.org/wiki/Machine_learning#36 | against an opponent). As it navigates its problem space, the program is provided feedback that's analogous to rewards, which it tries to maximise.[5]
Although each algorithm has advantages and limitations, no single algorithm works for all problems.[45][46][47]
Supervised learning
[edit]Supervised learning algorithms b... |
https://en.wikipedia.org/wiki/Machine_learning#37 | data, 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 matri... |
https://en.wikipedia.org/wiki/Machine_learning#38 | redict the output associated with new inputs.[49] An optimal function allows 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]
Types of su... |
https://en.wikipedia.org/wiki/Machine_learning#39 | he outputs are restricted to a limited set of values, while regression algorithms are used when the outputs can take any numerical value within a range. For example, in a classification algorithm that filters emails, the input is an incoming email, and the output is the folder in which to file the email. In contrast, r... |
https://en.wikipedia.org/wiki/Machine_learning#40 | eratures based on historical data.[51]
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 s... |
https://en.wikipedia.org/wiki/Machine_learning#41 | lgorithms find structures in data that has not been labelled, classified or categorised. 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. Central applications of unsupervised... |
https://en.wikipedia.org/wiki/Machine_learning#42 | 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, ofte... |
https://en.wikipedia.org/wiki/Machine_learning#43 | same cluster, and separation, the difference between clusters. Other methods are based on estimated density and graph connectivity.
A special type of unsupervised learning called, self-supervised learning involves training a model by generating the supervisory signal from the data itself.[53][54]
Semi-supervised learni... |
https://en.wikipedia.org/wiki/Machine_learning#44 | (with completely labelled training data). Some of the training examples are missing training labels, yet many machine-learning researchers have found that unlabelled data, when used in conjunction with a small amount of labelled data, can produce a considerable improvement in learning accuracy.
In weakly supervised lea... |
https://en.wikipedia.org/wiki/Machine_learning#45 | er effective training sets.[55]
Reinforcement learning
[edit]Reinforcement learning is an area of machine learning concerned with how software agents ought to take actions in an environment so as to maximise some notion of cumulative reward. Due to its generality, the field is studied in many other disciplines, such as... |
https://en.wikipedia.org/wiki/Machine_learning#46 | ntelligence, statistics and genetic algorithms. In reinforcement learning, the environment is typically represented as a Markov decision process (MDP). Many reinforcement learning algorithms use dynamic programming techniques.[56] Reinforcement learning algorithms do not assume knowledge of an exact mathematical model ... |
https://en.wikipedia.org/wiki/Machine_learning#47 | learning to play a game against a human opponent.
Dimensionality reduction
[edit]Dimensionality reduction is a process of reducing the number of random variables under consideration by obtaining a set of principal variables.[57] In other words, it is a process of reducing the dimension of the feature set, also called t... |
https://en.wikipedia.org/wiki/Machine_learning#48 | tion. One of the popular methods of dimensionality reduction is principal component analysis (PCA). PCA involves changing higher-dimensional data (e.g., 3D) to a smaller space (e.g., 2D). The manifold hypothesis proposes that high-dimensional data sets lie along low-dimensional manifolds, and many dimensionality reduct... |
https://en.wikipedia.org/wiki/Machine_learning#49 | approaches have been developed which do not fit neatly into this three-fold categorisation, and sometimes more than one is used by the same machine learning system. For example, topic modelling, meta-learning.[58]
Self-learning
[edit]Self-learning, as a machine learning paradigm was introduced in 1982 along with a neur... |
https://en.wikipedia.org/wiki/Machine_learning#50 | hout any external reward, by introducing emotion as an internal reward. Emotion is used as state evaluation of a self-learning agent. The CAA self-learning algorithm computes, in a crossbar fashion, both decisions about actions and emotions (feelings) about consequence situations. The system is driven by the interactio... |
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