Answers.Body: <p>You can also check <a href="https://www.researchgate.net/publication/328820423_Deep_Active_Learning_for_Text_Classification" rel="nofollow noreferrer">this paper</a>, which discusses the use of LSTM and GRU with Active Learning and word embeddings (word2vec).</p>
  
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Answers.CreationDate: 2020-09-19T21:12:30.643Z
Answers.LastActivityDate: 2020-09-19T21:12:30.643Z
Answers.UserDisplayName: ddaedalus
Answers.Body: <p>As I found this case backs to the <strong>sequence labeling</strong>. <a href="https://en.wikipedia.org/wiki/Sequence_labeling" rel="nofollow noreferrer">Sequence labeling</a> has some classic solution such as conditional random fields (CRFs) and hidden Markov model (HMM). Also, have some solution in Active learning (AL) which use from algorihtms such as struct SVM (<span class="math-container">$\text{SVM}^{\text{strcut}}$</span>) like <a href="https://link.springer.com/chapter/10.1007/978-3-540-70720-2_27" rel="nofollow noreferrer">this paper</a>.</p>
  
  <p>Also, some NLP solutions in active learning could help to solve these kind of problems such as <a href="https://www.semanticscholar.org/paper/A-study-of-active-learning-methods-for-named-entity-Chen-Lasko/3bce5270707e75380fba9907618315728ba5a4d8" rel="nofollow noreferrer">this paper</a> which is about active learning in named entity recognition (NER).</p>
  
  <p>Besides, the combination of active learning with Deep networks such as CNN happens. For example, <a href="https://openreview.net/forum?id=H1aIuk-RW" rel="nofollow noreferrer">this paper</a> explains more about the idea.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2018-12-06T15:09:29.217Z
Answers.LastActivityDate: 2018-12-06T15:21:53.940Z
Answers.LastEditDate: 2018-12-06T15:21:53.940Z
Answers.UserDisplayName: OmG
Body: <p>We have a series of data and we want to label the parts of each series. As we do not have any training data, we could try to use <em>active learning</em> as a solution, but the problem is that our classifier is something like RNN which needs a lot of data to be trained. Hence, we have a problem in converging fast to just label proportional small parts of unlabeled data. </p>
  
  <p>Is there any article about this problem (active learning and some complex classifiers, like RNN)? </p>
  
  <p>Is there any good solution to this problem or not? (as data is a series of actions)</p>
  
Tags: <machine-learning><recurrent-neural-networks><active-learning>
Title: How can active learning be used in the case of complex models that require a lot of data?
UserDisplayName: OmG
ThreadText: # How can active learning be used in the case of complex models that require a lot of data?
  
  (Asked by: username_0 on 2018-12-04)
  
  We have a series of data and we want to label the parts of each series. As we do not have any training data, we could try to use *active learning* as a solution, but the problem is that our classifier is something like RNN which needs a lot of data to be trained. Hence, we have a problem in converging fast to just label proportional small parts of unlabeled data.
  
  Is there any article about this problem (active learning and some complex classifiers, like RNN)?
  
  Is there any good solution to this problem or not? (as data is a series of actions)
  
  **Tags:** machine-learning, recurrent-neural-networks, active-learning
  
  ---
  
  ## 2 answers
  
  ### Answer by username_0 on 2018-12-06. Score: 3 | Accepted answer
  
  As I found this case backs to the **sequence labeling**. [Sequence labeling](https://en.wikipedia.org/wiki/Sequence_labeling) has some classic solution such as conditional random fields (CRFs) and hidden Markov model (HMM). Also, have some solution in Active learning (AL) which use from algorihtms such as struct SVM ($\text{SVM}^{\text{strcut}}$) like [this paper](https://link.springer.com/chapter/10.1007/978-3-540-70720-2_27).
  
  Also, some NLP solutions in active learning could help to solve these kind of problems such as [this paper](https://www.semanticscholar.org/paper/A-study-of-active-learning-methods-for-named-entity-Chen-Lasko/3bce5270707e75380fba9907618315728ba5a4d8) which is about active learning in named entity recognition (NER).
  
  Besides, the combination of active learning with Deep networks such as CNN happens. For example, [this paper](https://openreview.net/forum?id=H1aIuk-RW) explains more about the idea.
  
  
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  ### Answer by username_1 on 2020-09-19. Score: 0
  
  You can also check [this paper](https://www.researchgate.net/publication/328820423_Deep_Active_Learning_for_Text_Classification), which discusses the use of LSTM and GRU with Active Learning and word embeddings (word2vec).
  
  
  ---
  
  