Bootstrapping

By Thierry Bardini. 2000

639 citations recorded by the source corpus.

Part I. Premises: 1. A problematic picture of the personal interface 2. Social change and networks Part II. The Prehistory of the Laboratory: 3. Douglas C. Engelbart and the ARPA community 4. The augmentation framework and the relativist tradition Part III. Kinaesthetics and the Hypertextual Piano: Feeling the Code: 5. From physico-motor skills to kinaesthetic communication 6. The mouse is more than a pointing device Part IV. The Social Construction of the Personal Interface: 7. The beginnings of the hypermedium 8. The genesis of the graphic interface 9. The (inter)personal interface Part V. Coda: 10. When hand and memory meet again.

Subfields: Computer Science Applications, Information Systems and Management, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/ac3a42fddd287c7de791a7f52b5f0cacbf40f51a

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Citations in the corpus

Analyzing Co-training Style Algorithms (2007, background): “This tells that the conditional independence [5] or even the weak dependence [1] between the two views is not needed, at least, for iterative co-training which is the popular routine taken by many variants of co-training.”
Sentence Fusion for Multidocument News Summarization (2005, background): “…applications of multisequence alignment have been shown to increase the accuracy of the comparison in other NLP tasks (Barzilay and Lee 2002; Bangalore, Murdock, and Riccardi 2002; Lacatusu, Maiorano, and Harabagiu 2004); unlike our work these approaches operate on strings, not trees, and…”
Family‐Owned Business Succession: The Influence of Pre‐performance in the Nomination of Family and Nonfamily Members: Evidence from Spanish Firms (2010, background): “17For more details see: Efron (1979, 1982); Hall (1992); Efron and Tibshirani (1993); Mooney and Duval (1993); Shao and Tu (1995); Davison and Hinkley (1997); Chernick (1999).”
Cross-Domain Learning from Multiple Sources: A Consensus Regularization Perspective (2010, background): “In addition, Dasgupta et al. [25] and Abney [26], [27] provided PAC bounds on the error of co-Training in terms of the disagreement rate of hypotheses on unlabeled data in two independent views.”
Multi-View Intact Space Learning (2015, background): “Abney [16] showed that weak dependence can also guarantee successful co-training.”
Sign Language Recognition, Generation, and Translation: An Interdisciplinary Perspective (2019, background): “(Surveys of older work appear in [56] and [3].)”
Semi-Supervised Learning for Natural Language (2005, background): “Both theoretical (Blum and Mitchell, 1998; Dasgupta et al., 2001; Abney, 2002) and empirical analysis (Nigam and Ghani, 2000; Pierce and Cardie, 2001) have been done on co-training.”
Bilingual Co-Training for Sentiment Classification of Chinese Product Reviews (2011, background): “Abney (2002) shows that weak dependence between the two views can also guarantee successful co-training.”
The Social and Technological Dimensions of Scaffolding and Related Theoretical Concepts for Learning, Education, and Human Activity (2004, background): “1963) shortly before he and his group developed the first personal computer at Stanford Research Institute (SRI; Bardini, 2000; Hafner & Lyon, 1996; Waldrop, 2002).”
A Co-Regularization Approach to Semi-supervised Learning with Multiple Views (2005, background): “For more on agreement maximization principles, see (Abney, 2002; Dasgupta, Littman & McAllester, 2001; Collins & Singer, 1999; Yarowsky, 1995).”
Example Selection for Bootstrapping Statistical Parsers (2003, methodology): “Further avenues to explore include the development of selection methods to efﬁciently approximate maximizing the objective function of parser agreement on unlabeled data, following the work of Dasgupta et al. (2002) and Abney (2002).”
Inductive multi-task learning with multiple view data (2012, background): “Multi-view semi-supervised learning has attracted significant research interest in recent years [1, 5, 11, 14, 19, 23, 24].”
Bootstrapped Learning of Semantic Classes from Positive and Negative Examples (2003, neutral): “Abney (2002) gives a theoretical analysis of bootstrapping and co-training algorithms. A number of bootstrapping algorithms have been developed to learn semantic classes that may contain common nouns or GNs as well as PNs. Phillips and Riloff (2002) and others relied on structures such as appositive”
Multi-view clustering: A survey (2018, positive): “Thus, several weaker assumptions such as the weak conditional dependence assumption[20], much weaker “expansion” assumption[21], and the difference assumption[22] have been investigated.”