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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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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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Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
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Equity theory explains how our sense of fairness influences the dynamics of close relationships. Rooted in social psychology, the theory posits that individuals evaluate fairness by comparing the ratio of their contributions to the rewards they receive. Relationship satisfaction is highest when these ratios are perceived as balanced between partners, promoting mutual reciprocity and a sense of justice.Equity vs. Equality in RelationshipsEquity is distinct from equality. Fairness does not...
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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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Related Experiment Video

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Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
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A Framework of Learning Through Empirical Gain Maximization.

Yunlong Feng1, Qiang Wu2

  • 1Department of Mathematics and Statistics, State University of New York at Albany, Albany, NY 12222, U.S.A. ylfeng@albany.edu.

Neural Computation
|September 8, 2021
PubMed
Summary
This summary is machine-generated.

We introduce Empirical Gain Maximization (EGM) for robust regression, handling noisy data by approximating noise density. This framework unifies and explains existing robust methods, offering new insights and loss functions for machine learning.

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Area of Science:

  • Statistics
  • Machine Learning
  • Data Science

Background:

  • Robust regression is crucial for handling outliers and heavy-tailed noise in data.
  • Classical methods like maximum likelihood estimation can be sensitive to abnormal observations.
  • Existing robust nonconvex regression methods lack a unified theoretical understanding.

Purpose of the Study:

  • To develop a novel framework, Empirical Gain Maximization (EGM), for robust regression.
  • To provide a unified theoretical analysis for existing robust regression paradigms.
  • To establish new connections between robust loss functions and smoothing kernels.

Main Methods:

  • Approximating the noise distribution's density function instead of the true function.
  • Interpreting EGM from a minimum distance estimation perspective.
  • Reformulating established robust regression methods within the EGM framework.

Main Results:

  • Demonstrated that Tukey regression and truncated least squares regression fit within the EGM framework.
  • Established a learning theory for EGM, enabling unified analysis of robust regression approaches.
  • Revealed a close relationship between Tukey's biweight loss and the triweight kernel, deriving the former from the latter.

Conclusions:

  • The EGM framework offers a new perspective on bounded nonconvex loss functions.
  • Existing loss functions like truncated square, Geman-McClure, and exponential squared can be linked to smoothing kernels.
  • The EGM framework facilitates the development of novel bounded nonconvex loss functions for enhanced robust learning.