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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...
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...
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...
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...
Higher Mental Functions of Brain: Learning and Memory01:26

Higher Mental Functions of Brain: Learning and Memory

Memory is one of the most vital higher mental functions of the brain. Memory is closely related to learning because it enables us to retain information and experiences from our past to use them in our present life. It also helps us to remember facts, events, and skills, such as riding a bike or swimming. There are two types of memory — declarative memory, which involves memorizing facts or events, and procedural memory, which enables us to remember how to do something like writing or playing an...
Information Processing Approach01:30

Information Processing Approach

The information-processing theory of cognitive development centers on fundamental mental processes, including attention, memory, and problem-solving skills. Researchers in this field examine how cognitive abilities, such as working memory, evolve and influence children's overall development. Studies indicate that children with stronger working memory tend to excel in reading comprehension, math, and problem-solving compared to peers with less efficient memory skills. Low working memory is also...

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Related Experiment Videos

A new learning paradigm: learning using privileged information.

Vladimir Vapnik1, Akshay Vashist

  • 1NEC Labs America, 4 Independence Way, Princeton, NJ 08540, United States. vlad@nec-labs.com

Neural Networks : the Official Journal of the International Neural Network Society
|July 28, 2009
PubMed
Summary
This summary is machine-generated.

Learning Using Hidden Information (LUHI) enhances machine learning by incorporating teacher-provided "hidden information" beyond standard examples. This advanced paradigm demonstrates superior performance in practical applications compared to classical methods.

Related Experiment Videos

Area of Science:

  • Machine Learning
  • Artificial Intelligence
  • Computer Science

Background:

  • The classical machine learning paradigm assigns a limited role to the teacher.
  • Vapnik introduced the Learning Using Hidden Information (LUHI) paradigm in the second edition of "Estimation of Dependences Based on Empirical Data."
  • LUHI extends Support Vector Machine (SVM) methods with the SVM(gamma)+ approach.

Purpose of the Study:

  • To detail the LUHI learning paradigm and its associated algorithms.
  • To introduce novel algorithms within the LUHI framework.
  • To explore specific forms of privileged information and their application.

Main Methods:

  • Discussion of the LUHI paradigm and its theoretical underpinnings.
  • Development and presentation of new algorithms for LUHI.
  • Implementation of SVM(gamma)+ for LUHI algorithms.

Main Results:

  • Demonstration of the superiority of LUHI over classical machine learning in practical problem-solving.
  • Analysis of the impact of various forms of privileged information.
  • Validation of the advanced learning paradigm's effectiveness.

Conclusions:

  • The LUHI paradigm, incorporating hidden information from teachers, offers significant advantages over traditional machine learning.
  • New algorithms and methods enhance the practical applicability of LUHI.
  • Further research into privileged information can unlock greater potential in AI.