Learning without a teacher

Spragins, J.

doi:10.1109/tit.1966.1053865

Cited by 75 publications

(3 citation statements)

References 12 publications

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“…The concepts of supervised and unsupervised learning have long existed in psychology [23], [24]. The earliest computational distinction between the two was first introduced by Spragins in 1966, albeit without modern terminology, i.e., Spragins distinguished the two as learning with or without a teacher [25]. The modern machine learning distinction was first introduced by Darling and Raudseps in 1970, with an emphasis on classification [26].…”

Section: Supervised and Unsupervised Learningmentioning

confidence: 99%

A Critical Analysis of Unsupervised Learning in the Context of Raven's Progressive Matrices

Omari,

Mckay,

Gedeon

et al. 2024

Preprint

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Section: Supervised and Unsupervised Learningmentioning

confidence: 99%

A Critical Analysis of Unsupervised Learning in the Context of Raven's Progressive Matrices

Omari,

Mckay,

Gedeon

et al. 2024

Preprint

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“…The idea of first partitioning unlabeled samples, and then assigning labels to each partition, was thoroughly explored in the sixties from the perspective of both signal-processing [59,60,61] and potential functions [62,63,64]. In 1966 Dorofeyuk presented several clustering algorithms, and then assigned labels to each cluster according to the known majority label in each cluster.…”

Section: Supervisedmentioning

confidence: 99%

Adaptive and Interactive Approaches to Document Analysis

Nagy¹,

Veeramachaneni

2008

Machine Learning in Document Analysis and Recognition

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Summary. This chapter explores three aspects of learning in document analysis: (1) field classification, (2) interactive recognition, and (3) portable and networked applications. Context in document classification conventionally refers to language context, i.e., deterministic or statistical constraints on the sequence of letters in syllables or words, and on the sequence of words in phrases or sentences. We show how to exploit other types of statistical dependence, specifically the dependence between the shape features of several patterns due to the common source of the patterns within a field or a document. This type of dependence leads to field classification, where the features of some patterns may reveal useful information about the features of other patterns from the same source but not necessarily from the same class. We explore the relationship between field classification and the older concepts of unsupervised learning and adaptation. Human interaction is often more effective interspersed with algorithmic processes than only before or after the automated parts of the process. We develop a taxonomy for interaction during training and testing, and show how either human-initiated and machine-initiated interaction can lead to human and machine learning. In a section on new technologies, we discuss how new cameras and displays, web-wide access, interoperability, and essentially unlimited storage provide fertile new approaches to document analysis.

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“…Therefore some alternat'ive approaches have been developed [7]. In the following, some methods of estimating H, and E,,, one type of approach to the nonsupervised problem, are described.…”

Section: Synchronized Nonsupervised Learningmentioning

confidence: 99%

Synchronization for Unknown Signal Sequence by Learning Procedure

Tamura

Tanaka

1972

IEEE Trans. Commun.

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Generalization of a technique for binary only for thc baseband data transmission. However, the (Concise Papers), vel, COM-14, pp, 6748, Feb, 1966, data communicalion," IEEE Trccns. Commzot. Technol. /> results obtained in the baseband system are extendible i<,[61 H. Miyaknwa and H. Har:tshima, "A mcthod of code conto carrier s y s t e m such as FM and SSB systems. ;y q,*,j ycrsion for digital communication channel with intersymbol Interference" (in Japanese) Trcm. Znst. Electr n . Conqnz~n Eng.

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Learning without a teacher

Cited by 75 publications

References 12 publications

A Critical Analysis of Unsupervised Learning in the Context of Raven's Progressive Matrices

A Critical Analysis of Unsupervised Learning in the Context of Raven's Progressive Matrices

Adaptive and Interactive Approaches to Document Analysis

Synchronization for Unknown Signal Sequence by Learning Procedure

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