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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...
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...
Ranks01:02

Ranks

Unlike parametric methods, nonparametric statistics are ideal for nominal and ordinal data, requiring fewer assumptions about the population's nature or distribution. This makes nonparametric methods easier to apply and interpret, as they do not depend on parameters like mean or standard deviation. One common approach in nonparametric analysis is to sort data according to a specific criterion. For instance, we might arrange weather data from hottest to coldest days in a month or rank cities...
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...

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Related Experiment Video

Updated: May 30, 2026

Appetitive Associative Olfactory Learning in Drosophila Larvae
09:22

Appetitive Associative Olfactory Learning in Drosophila Larvae

Published on: February 18, 2013

SortNet: learning to rank by a neural preference function.

Leonardo Rigutini1, Tiziano Papini, Marco Maggini

  • 1Dipartimento di Ingegneria dell’Informazione, Università degli Studi di Siena, Siena, Italy. rigutini@dii.unisi.it

IEEE Transactions on Neural Networks
|July 22, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a novel neural network for learning to rank, adapting to user preferences by learning object comparisons. The comparative neural network (CmpNN) improves personalized retrieval systems by effectively ranking items based on user feedback.

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Published on: August 18, 2014

Area of Science:

  • Information Retrieval
  • Machine Learning
  • Artificial Intelligence

Background:

  • Relevance ranking is crucial for personalized retrieval systems where user preferences vary.
  • Existing learning-to-rank methods include scoring functions and pairwise approaches.
  • Adaptive algorithms are needed to handle dynamic and user-specific relevance criteria.

Purpose of the Study:

  • To present a novel preference learning method for learning to rank.
  • To introduce the comparative neural network (CmpNN) for approximating pairwise comparison functions.
  • To enhance ranking performance through an active-learning procedure.

Main Methods:

  • A comparative neural network (CmpNN) is trained on examples to learn a preference function.
  • The CmpNN utilizes a specific architecture to capture symmetries in preference functions.
  • An active-learning strategy is employed to select the most informative training data patterns.

Main Results:

  • The learned preference function is integrated into a sorting algorithm for global object ranking.
  • The proposed method demonstrates promising performance on the LETOR dataset.
  • The CmpNN approach shows competitive results compared to other state-of-the-art algorithms.

Conclusions:

  • The CmpNN provides an effective approach for preference learning in ranking tasks.
  • The active-learning procedure enhances the efficiency and performance of the ranking algorithm.
  • This method offers a robust solution for personalized information retrieval systems.