User:An anonymous user with secrets/sandbox/Error-driven learning

Error-driven learning is a type of reinforcement learning algorithm. This algorithm adjusts the parameters of a model based on the difference between the desired and actual outputs. These models are characterized by relying on feedback from its environment rather than explicit labels or categories.^[1] They are based on the idea that language acquisition involves the minimization of the prediction error (MPSE).^[2] Through these prediction errors, these models keep adjusting expectations and simplify computational complexity. These algorithms are usually run by the GeneRec algorithm.^[3]

Error-driven learning is the basis for a vast array of computational models in the brain and cognitive sciences. ^[2] Error driven learning algorithms are widely used in various domains such as, computer vision, bioinformatics. These methods have also been successfully applied in many areas of natural language processing (NLP), including part-of-speech tagging^[4], parsing^[4] named entity recognition (NER)^[5], machine translation (MT)^[6], speech recognition (SR)^[4] and and dialogue systems^[7].

Formal Definition

Error-driven learning is a type of learning that relies on the feedback of prediction errors to adjust the expectations or parameters of a model. The key components of error-driven learning include the following:

A set $S$ of states representing the different situations that the learner can encounter.
A set $A$ of actions that the learner can take in each state.
A prediction function $P(s,a)$ that gives the learner’s current prediction of the outcome of taking action $a$ in state $s$ .
An error function $E(o,p)$ that compares the actual outcome $o$ with the prediction $p$ and produces an error value.
An update rule $U(p,e)$ that adjusts the prediction $p$ in light of the error $e$ .^[2]

Algorithms

Error driven learning algorithms are a class of machine learning algorithms that use the difference between the actual output and the desired output of a system to adjust the parameters of the system. Error driven learning algorithms are often used in supervised learning, where the system is given a set of input-output pairs and learns to generalize from them.^[2]

The most common error backpropagation learning algorithm is the GeneRec; It is generalized recirculation algorithm, which is used for gene prediction in DNA sequences. Most other error-driven learning algorithms use an alternative version of GeneRec.^[3]

Applications

Cognitive science

Many simple error-driven learning models turn out to be able to explain seemingly complex phenomena of human cognition and sometimes, predict behavior that more optimal and rational models or more complex networks fail to explain. They provide a powerful and flexible way to model the learning process in the brain and explain various phenomena such as perception, attention, memory and decision-making. By using the error as a signal to guide the learning process, error driven learning algorithms can capture the statistical regularities and structure of the environment, and adapt to the changing demands and goals of the agent. ^[2]

Moreover, cognitive science has inspired the development of new error driven learning algorithms and architectures that are more biologically plausible and computationally efficient, such as deep belief networks, spiking neural networks, reservoir computing, etc1. These algorithms and architectures are motivated by the principles and constraints of the brain and the nervous system, and aim to capture the emergent properties and dynamics of the neural circuits and systems.^[2]^[8]

NLPs

Part-of-speech tagging

Part-of-speech (POS) tagging is a crucial component in Natural Language Processing (NLP). It helps resolve human language ambiguity at different analysis levels and its output (tagged data) can be used in various applications of NLP such as Information Extraction, Information Retrieval, Question Answering, Speech Recognition, Text-to-speech conversion, Partial Parsing, Machine Translation, and Grammar Correction.^[4]

Parsing

Parsing task of analyzing the syntactic structure of a sentence and producing a tree representation that shows how the words are related. Error-driven learning can help the model learn from its errors and adjust its parameters to produce more accurate parses.^[4]

*A sentence is made up of multiple phrases and each phrase, in turn, is made of phrases or words. Each phrase has a head word which may have strong syntactic re- lations with oth

er words in the sentence. Consider the phrases, her hard work and the hard surface. The head words work and surface are indicative of the calling for stamina/endurance and not easily penetrable senses of hard.*

Named entity recognition (NER)

NER is the task of identifying and classifying entities (such as persons, locations, organizations, etc.) in a text. Error-driven learning can help the model learn from its false positives and false negatives and improve its recall and precision on (NER).^[5]

Machine translation

This is the task of translating a text from one language to another. Error-driven learning can help the model learn from its translation errors and improve its quality and fluency.^[6]

Speech recognition

This is the task of converting spoken words into written text. Error-driven learning can help the model learn from its recognition errors and improve its accuracy and robustness.^[4]

Dialogue systems

These are systems that can interact with humans using natural language, such as chatbots, virtual assistants, or conversational agents. Error-driven learning can help the model learn from its dialogue errors and improve its understanding and generation abilities.^[7]

Advantages

Error-driven learning has several advantages over other types of machine learning algorithms:

They can learn from feedback and correct their mistakes, which makes them adaptive and robust to noise and changes in the data.
They can handle large and high-dimensional data sets, as they do not require explicit feature engineering or prior knowledge of the data distribution.
They can achieve high accuracy and performance, as they can learn complex and nonlinear relationships between the input and the output.^[2]

Limitations

They can suffer from overfitting, which means that they memorize the training data and fail to generalize to new and unseen data. This can be mitigated by using regularization techniques, such as adding a penalty term to the loss function, or reducing the complexity of the model.^[9]

They can be sensitive to the choice of the error function, the learning rate, the initialization of the weights, and other hyperparameters, which can affect the convergence and the quality of the solution. This requires careful tuning and experimentation, or using adaptive methods that adjust the hyperparameters automatically.

They can be computationally expensive and time-consuming, especially for nonlinear and deep models, as they require multiple iterations and calculations to update the weights of the system. This can be alleviated by using parallel and distributed computing, or using specialized hardware such as GPUs or TPUs.^[2]

References

^ Sadre, Ramin; Pras, Aiko (2009-06-19). Scalability of Networks and Services: Third International Conference on Autonomous Infrastructure, Management and Security, AIMS 2009 Enschede, The Netherlands, June 30 - July 2, 2009, Proceedings. Springer. ISBN 978-3-642-02627-0.
^ ^a ^b ^c ^d ^e ^f ^g ^h Hoppe, Dorothée B.; Hendriks, Petra; Ramscar, Michael; van Rij, Jacolien (2022-10-01). "An exploration of error-driven learning in simple two-layer networks from a discriminative learning perspective". Behavior Research Methods. 54 (5): 2221–2251. doi:10.3758/s13428-021-01711-5. ISSN 1554-3528. PMC 9579095. PMID 35032022.{{cite journal}}: CS1 maint: PMC format (link)
^ ^a ^b O'Reilly, Randall C. (1996-07-01). "Biologically Plausible Error-Driven Learning Using Local Activation Differences: The Generalized Recirculation Algorithm". Neural Computation. 8 (5): 895–938. doi:10.1162/neco.1996.8.5.895. ISSN 0899-7667.
^ ^a ^b ^c ^d ^e ^f Mohammad, Saif, and Ted Pedersen. "Combining lexical and syntactic features for supervised word sense disambiguation." Proceedings of the Eighth Conference on Computational Natural Language Learning (CoNLL-2004) at HLT-NAACL 2004. 2004. APA
^ ^a ^b Florian, Radu, et al. "Named entity recognition through classifier combination." Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003. 2003.
^ ^a ^b Rozovskaya, Alla, and Dan Roth. "Grammatical error correction: Machine translation and classifiers." Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2016.
^ ^a ^b Iosif, Elias; Klasinas, Ioannis; Athanasopoulou, Georgia; Palogiannidi, Elisavet; Georgiladakis, Spiros; Louka, Katerina; Potamianos, Alexandros (2018-01-01). "Speech understanding for spoken dialogue systems: From corpus harvesting to grammar rule induction". Computer Speech & Language. 47: 272–297. doi:10.1016/j.csl.2017.08.002. ISSN 0885-2308.
^ Bengio, Y. (2009). Learning deep architectures for AI. Foundations and trends® in Machine Learning, 2(1), 1-127
^ Ajila, Samuel A.; Lung, Chung-Horng; Das, Anurag (2022-06-01). "Analysis of error-based machine learning algorithms in network anomaly detection and categorization". Annals of Telecommunications. 77 (5): 359–370. doi:10.1007/s12243-021-00836-0. ISSN 1958-9395.

Category:Machine learning algorithms

[:0-1] Sadre, Ramin; Pras, Aiko (2009-06-19). Scalability of Networks and Services: Third International Conference on Autonomous Infrastructure, Management and Security, AIMS 2009 Enschede, The Netherlands, June 30 - July 2, 2009, Proceedings. Springer. ISBN 978-3-642-02627-0.

[:1-2] ^ ^a ^b ^c ^d ^e ^f ^g ^h Hoppe, Dorothée B.; Hendriks, Petra; Ramscar, Michael; van Rij, Jacolien (2022-10-01). "An exploration of error-driven learning in simple two-layer networks from a discriminative learning perspective". Behavior Research Methods. 54 (5): 2221–2251. doi:10.3758/s13428-021-01711-5. ISSN 1554-3528. PMC 9579095. PMID 35032022.{{cite journal}}: CS1 maint: PMC format (link)

[:6-3] O'Reilly, Randall C. (1996-07-01). "Biologically Plausible Error-Driven Learning Using Local Activation Differences: The Generalized Recirculation Algorithm". Neural Computation. 8 (5): 895–938. doi:10.1162/neco.1996.8.5.895. ISSN 0899-7667.

[:2-4] ^ ^a ^b ^c ^d ^e ^f Mohammad, Saif, and Ted Pedersen. "Combining lexical and syntactic features for supervised word sense disambiguation." Proceedings of the Eighth Conference on Computational Natural Language Learning (CoNLL-2004) at HLT-NAACL 2004. 2004. APA

[:3-5] Florian, Radu, et al. "Named entity recognition through classifier combination." Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003. 2003.

[:4-6] Rozovskaya, Alla, and Dan Roth. "Grammatical error correction: Machine translation and classifiers." Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2016.

[:5-7] Iosif, Elias; Klasinas, Ioannis; Athanasopoulou, Georgia; Palogiannidi, Elisavet; Georgiladakis, Spiros; Louka, Katerina; Potamianos, Alexandros (2018-01-01). "Speech understanding for spoken dialogue systems: From corpus harvesting to grammar rule induction". Computer Speech & Language. 47: 272–297. doi:10.1016/j.csl.2017.08.002. ISSN 0885-2308.

[8] Bengio, Y. (2009). Learning deep architectures for AI. Foundations and trends® in Machine Learning, 2(1), 1-127

[9] Ajila, Samuel A.; Lung, Chung-Horng; Das, Anurag (2022-06-01). "Analysis of error-based machine learning algorithms in network anomaly detection and categorization". Annals of Telecommunications. 77 (5): 359–370. doi:10.1007/s12243-021-00836-0. ISSN 1958-9395.

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