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Related Concept Videos

Associative Learning01:27

Associative Learning

2.0K
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...
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Observational Learning01:12

Observational Learning

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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...
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Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

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Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
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Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
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Cognitive Learning01:21

Cognitive Learning

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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...
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Purposive Learning01:22

Purposive Learning

688
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...
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Related Experiment Videos

Clustered Multi-Task Learning Via Alternating Structure Optimization.

Jiayu Zhou1, Jianhui Chen1, Jieping Ye1

  • 1Computer Science and Engineering, Arizona State University, Tempe, AZ 85287.

Advances in Neural Information Processing Systems
|October 21, 2014
PubMed
Summary
This summary is machine-generated.

This study reveals an equivalence between Alternating Structure Optimization (ASO) and Clustered Multi-Task Learning (CMTL). A new convex formulation of CMTL is more efficient for high-dimensional data.

Related Experiment Videos

Area of Science:

  • Machine Learning
  • Data Science
  • Artificial Intelligence

Background:

  • Multi-task learning (MTL) enhances model generalization by training on related tasks concurrently.
  • Alternating Structure Optimization (ASO) is a prevalent MTL method using shared low-dimensional structures.
  • Clustered Multi-Task Learning (CMTL) assumes tasks form unknown clusters of similarity.

Purpose of the Study:

  • To establish the theoretical equivalence between ASO and CMTL.
  • To introduce a novel, efficient convex relaxation for CMTL.
  • To demonstrate the practical advantages of the proposed CMTL formulation.

Main Methods:

  • Mathematical derivation to prove the equivalence of ASO and CMTL objectives.
  • Development of a convex relaxation for the non-convex CMTL formulation.
  • Implementation and evaluation of three algorithms for solving the convex CMTL.

Main Results:

  • Demonstrated a significant equivalence relationship between ASO and CMTL.
  • Proposed a convex CMTL formulation that is more efficient than ASO relaxations, especially for high-dimensional data.
  • Experimental validation on benchmark datasets confirmed the efficiency of the developed algorithms.

Conclusions:

  • The equivalence provides new theoretical insights into MTL.
  • The efficient convex CMTL formulation offers a practical advancement for high-dimensional MTL problems.
  • The developed algorithms effectively solve the proposed CMTL formulation.