Content deleted Content added
Mention crowdsourcing and human-in-the-loop learning. |
Citation bot (talk | contribs) Add: s2cid, authors 1-1. Removed parameters. Some additions/deletions were parameter name changes. | Use this bot. Report bugs. | Suggested by AManWithNoPlan | #UCB_CommandLine |
||
Line 63:
== Scenarios ==
*'''Membership Query Synthesis''': This is where the learner generates its own instance from an underlying natural distribution. For example, if the dataset are pictures of humans and animals, the learner could send a clipped image of a leg to the teacher and query if this appendage belongs to an animal or human. This is particularly useful if the dataset is small.<ref>{{Cite journal|last1=Wang|first1=Liantao|last2=Hu|first2=Xuelei|last3=Yuan|first3=Bo|last4=Lu|first4=Jianfeng|date=2015-01-05|title=Active learning via query synthesis and nearest neighbour search|url=http://espace.library.uq.edu.au/view/UQ:344582/UQ344582_OA.pdf|journal=Neurocomputing|volume=147|pages=426–434|doi=10.1016/j.neucom.2014.06.042|s2cid=3027214 }}</ref>
*'''Pool-Based Sampling''': In this scenario, instances are drawn from the entire data pool and assigned a confidence score, a measurement of how well the learner “understands” the data. The system then selects the instances for which it is the least confident and queries the teacher for the labels.
*'''Stream-Based Selective Sampling''': Here, each unlabeled data point is examined one at a time with the machine evaluating the informativeness of each item against its query parameters. The learner decides for itself whether to assign a label or query the teacher for each datapoint.
Line 80:
*'''Conformal predictors''': [[conformal prediction|This method]] predicts that a new data point will have a label similar to old data points in some specified way and degree of the similarity within the old examples is used to estimate the confidence in the prediction.<ref>{{Cite journal|last1=Makili|first1=Lázaro Emílio|last2=Sánchez|first2=Jesús A. Vega|last3=Dormido-Canto|first3=Sebastián|date=2012-10-01|title=Active Learning Using Conformal Predictors: Application to Image Classification|journal=Fusion Science and Technology|volume=62|issue=2|pages=347–355|doi=10.13182/FST12-A14626|s2cid=115384000|issn=1536-1055}}</ref>
*'''Mismatch-first farthest-traversal''': The primary selection criterion is the prediction mismatch between the current model and nearest-neighbour prediction. It targets on wrongly predicted data points. The second selection criterion is the distance to previously selected data, the farthest first. It aims at optimizing the diversity of selected data.<ref name='zhaos' />
*'''User Centered Labeling Strategies:''' Learning is accomplished by applying dimensionality reduction to graphs and figures like scatter plots. Then the user is ask to label the complied data (categorical, numerical, relevences scores, relation between two instances.<ref name=":3">{{Cite journal |
A wide variety of algorithms have been studied that fall into these categories.<ref name="settles" /><ref name="olsson" />
Line 95:
{{reflist |refs=
<ref name="hybrid">{{cite journal |last1=Lughofer |first1=Edwin |title=Hybrid active learning for reducing the annotation effort of operators in classification systems |journal=Pattern Recognition |date=February 2012 |volume=45 |issue=2 |pages=884–896 |doi=10.1016/j.patcog.2011.08.009|bibcode=2012PatRe..45..884L }}</ref>
<ref name="Bouneffouf(2014)">{{cite book |first1=Djallel |last1=Bouneffouf |first2=Romain |last2=Laroche |first3=Tanguy |last3=Urvoy |first4=Raphael |last4=Féraud |first5=Robin |last5=Allesiardo |year=2014 |chapter-url=https://hal.archives-ouvertes.fr/hal-01069802 |chapter=Contextual Bandit for Active Learning: Active Thompson |doi=10.1007/978-3-319-12637-1_51 |isbn=978-3-319-12636-4 |id=HAL Id: hal-01069802 |editor=Loo, C. K. |editor2=Yap, K. S. |editor3=Wong, K. W. |editor4=Teoh, A. |editor5=Huang, K. |title=Neural Information Processing |volume=8834 |pages=405–412 |series=Lecture Notes in Computer Science |s2cid=1701357 |url=https://hal.archives-ouvertes.fr/hal-01069802/file/Contextual_Bandit_for_Active_Learning.pdf }}</ref>
<ref name="multi">{{cite book |doi=10.1145/1557019.1557119 |isbn=978-1-60558-495-9 |chapter-url=https://www.microsoft.com/en-us/research/wp-content/uploads/2009/01/sigkdd09-yang.pdf|chapter=Effective multi-label active learning for text classification |title=Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '09 |pages=917 |year=2009 |last1=Yang |first1=Bishan |last2=Sun |first2=Jian-Tao |last3=Wang |first3=Tengjiao |last4=Chen |first4=Zheng |citeseerx=10.1.1.546.9358 |s2cid=1979173 }}</ref>
<ref name="single-pass">{{Cite journal | doi=10.1007/s12530-012-9060-7 |title = Single-pass active learning with conflict and ignorance| journal=Evolving Systems| volume=3| issue=4| pages=251–271|year = 2012|last1 = Lughofer|first1 = Edwin|s2cid = 43844282}}</ref>
| |||