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Related Concept Videos

Purposive Learning01:22

Purposive Learning

E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a bonus...
Associative Learning01:27

Associative Learning

Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
Observational Learning01:12

Observational Learning

Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning because...
Cognitive Learning01:21

Cognitive Learning

Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
Introduction to Learning01:18

Introduction to Learning

Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
Language Development01:22

Language Development

Children master language quickly and with relative ease, supported by both biological predisposition and reinforcement. B. F. Skinner (1957) proposed that language is learned through reinforcement, while Noam Chomsky (1965) argued that language acquisition mechanisms are biologically determined.
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Related Experiment Video

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Transcranial Direct Current Stimulation (tDCS) of Wernicke's and Broca's Areas in Studies of Language Learning and Word Acquisition
12:49

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Published on: July 13, 2019

Task-driven dictionary learning.

Julien Mairal1, Francis Bach, Jean Ponce

  • 1Department of Statistics, University of California, 301 Evans Hall, Berkeley, CA 94720-3860, USA. julien@stat.berkeley.edu

IEEE Transactions on Pattern Analysis and Machine Intelligence
|August 3, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a new supervised dictionary learning method for sparse data representations. The efficient algorithm effectively tunes dictionaries for various tasks, including classification and regression.

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Area of Science:

  • Machine Learning
  • Neuroscience
  • Signal Processing

Background:

  • Sparse representations using learned dictionaries are effective for signal restoration tasks.
  • Supervised tuning of dictionaries for tasks beyond restoration, like classification, is challenging.

Purpose of the Study:

  • To present a general formulation for supervised dictionary learning adaptable to diverse tasks.
  • To introduce an efficient algorithm for solving the supervised dictionary learning optimization problem.

Main Methods:

  • Developed a general formulation for supervised dictionary learning.
  • Proposed an efficient optimization algorithm for the learning problem.
  • Applied the method to various tasks including classification and regression.

Main Results:

  • Demonstrated effectiveness in large-scale settings.
  • Showcased suitability for supervised and semi-supervised classification.
  • Validated performance on regression tasks for sparse data representations.

Conclusions:

  • The proposed supervised dictionary learning approach is effective across multiple domains.
  • The efficient algorithm facilitates large-scale applications of supervised dictionary learning.