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Associative Learning01:27

Associative Learning

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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

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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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Introduction to Learning01:18

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

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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...
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Cognitive Learning01:21

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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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Inductive Reasoning00:59

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Inductive reasoning is a form of logical thinking that uses related observations to arrive at a general conclusion. It is uncertain and operates in degrees to which the conclusions are credible. As such, inductive arguments can be weak or strong, rather than valid or invalid, and conclusions can be used to formulate testable, falsifiable hypotheses.
Inductive reasoning is common in descriptive science. A life scientist makes observations and records them. This data can be qualitative or...
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Related Experiment Video

Updated: May 2, 2026

Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task
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Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task

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Kernel learning at the first level of inference.

Gavin C Cawley1, Nicola L C Talbot1

  • 1School of Computing Sciences, University of East Anglia, Norwich, NR4 7TJ, UK.

Neural Networks : the Official Journal of the International Neural Network Society
|February 25, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces a novel kernel learning method for Least-Squares Support Vector Machine (LS-SVM) classifiers. Jointly optimizing kernel and expansion coefficients at the first inference level significantly reduces overfitting in model selection.

Keywords:
Automatic relevance determinationKernel methodsModel selectionOver-fittingRegularisation

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Area of Science:

  • Machine Learning
  • Computational Statistics

Background:

  • Kernel learning methods involve multi-level inference for parameter tuning.
  • Model selection in kernel machines risks overfitting, especially with many parameters like in Automatic Relevance Determination (ARD).
  • Conventional methods often rely on cross-validation or theoretical bounds for parameter tuning.

Purpose of the Study:

  • To investigate learning kernel parameters at the first inference level for Least-Squares Support Vector Machine (LS-SVM) classifiers.
  • To jointly optimize kernel parameters and expansion coefficients, minimizing a regularized training criterion.
  • To alleviate the problem of overfitting the model selection criterion by reducing the number of parameters to tune.

Main Methods:

  • Joint optimization of kernel parameters and kernel expansion coefficients.
  • Introduction of a regularisation term acting on kernel parameters.
  • Evaluation on synthetic and real-world binary classification benchmark problems.

Main Results:

  • Kernel learning at the first inference level demonstrates statistically superior performance compared to conventional approaches.
  • The proposed method significantly alleviates the risk of overfitting the model selection criterion.
  • The approach is competitive with Multiple Kernel Learning but offers reduced computational cost.

Conclusions:

  • Learning kernel parameters at the first inference level is an effective strategy for LS-SVM classifiers.
  • This method improves generalization performance by mitigating model selection overfitting.
  • The approach offers a computationally efficient and statistically robust alternative to existing kernel learning techniques.