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Related Concept Videos

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...
Correlation and Regression00:53

Correlation and Regression

In statistics, correlation describes the degree of association between two variables. In the subfield of linear regression, correlation is mathematically expressed by the correlation coefficient, which describes the strength and direction of the relationship between two variables. The coefficient is symbolically represented by 'r' and ranges from -1 to +1. A positive value indicates a positive correlation where the two variables move in the same direction. A negative value suggests a negative...
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...
Correlations02:20

Correlations

Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between...
Correlation01:09

Correlation

In statistics, two variables are said to be correlated if the values of one variable are associated with the other variable. Depending on the relationship between two variables, correlation can be of three types– positive correlation, negative correlation, and zero correlation.
Two variables, for example, a and b, are said to be positively correlated if both variables move in the same direction. In other words, a positive correlation exists between two variables, a and b, if:
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...

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Related Experiment Videos

Fast initialization for cascade-correlation learning.

M Lehtokangas1

  • 1Signal Processing Laboratory, Tampere University of Technology, FIN-33101 Tampere, Finland.

IEEE Transactions on Neural Networks
|February 7, 2008
PubMed
Summary

A new weight initialization method for cascade-correlation learning, based on stepwise regression, significantly speeds up training. This approach maintains or improves overall performance compared to traditional candidate training methods.

Related Experiment Videos

Area of Science:

  • Machine Learning
  • Artificial Intelligence
  • Neural Networks

Background:

  • Cascade-correlation learning requires effective weight initialization.
  • Traditional candidate training for initialization can be computationally expensive due to training numerous candidate units.

Purpose of the Study:

  • To introduce a novel and efficient weight initialization method for cascade-correlation learning.
  • To address the computational cost associated with conventional initialization techniques.

Main Methods:

  • The proposed method utilizes the principles of stepwise regression for weight initialization.
  • Empirical simulations were conducted to evaluate the new approach.

Main Results:

  • The stepwise regression-based method significantly accelerates cascade-correlation learning compared to candidate training.
  • The performance of the network using the new initialization method was comparable or superior to candidate training.

Conclusions:

  • Stepwise regression offers a computationally efficient and effective alternative for weight initialization in cascade-correlation learning.
  • The proposed method provides a practical solution for speeding up neural network training without compromising performance.