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Related Concept Videos

Forgetting01:21

Forgetting

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Forgetting is an intrinsic aspect of human memory, characterized by the gradual loss or inaccessibility of information over time. Hermann Ebbinghaus, a pioneering psychologist, extensively studied this phenomenon and formulated the forgetting curve. This curve illustrates that memory loss occurs rapidly immediately after learning and then decelerates over time. Several mechanisms contribute to forgetting, including encoding failure, storage decay, retrieval failure, and interference.
Encoding...
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Interference and Decay01:16

Interference and Decay

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Forgetting is a complex cognitive phenomenon influenced by several factors, among which interference and decay are particularly prominent. These processes explain why individuals often struggle to retrieve specific information from memory, leading to lapses in recall that can be observed in everyday situations.
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Weighted Mean00:57

Weighted Mean

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While taking the arithmetic, geometric, or harmonic mean of a sample data set, equal importance is assigned to all the data points. However, all the values may not always be equally important in some data sets. An intrinsic bias might make it more important to give more weightage to specific values over others.
For example, consider the number of goals scored in the matches of a tournament. While computing the average number of goals scored in the tournament, it may be more important to...
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Expected Value01:15

Expected Value

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The expected value is known as the "long-term" average or mean. This means that over the long term of experimenting over and over, you would expect this average. The expected average is represented by the symbol μ. It is calculated as follows:In the equation, x is an event, and P(x) is the probability of the event occurring.The expected value has practical applications in decision theory.This text is adapted from Openstax, Introductory Statistics, Section 4.2 Mean or Expected Value and...
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Methods of Medium Optimization01:28

Methods of Medium Optimization

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Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...
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Hindsight Biases01:12

Hindsight Biases

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Hindsight bias leads you to believe that the event you just experienced was predictable, even though it really wasn’t. In other words, you knew all along that things would turn out the way they did. Can you relate this to the phrase "Hindsight is 20/20" now? 
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Online Reinforcement Learning Using a Probability Density Estimation.

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A Prediction Error-driven Retrieval Procedure for Destabilizing and Rewriting Maladaptive Reward Memories in Hazardous Drinkers
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Online EM with weight-based forgetting.

Enric Celaya1, Alejandro Agostini

  • 1Institut de Robòtica i Informàtica Industrial (CSIC-UPC), 08028 Barcelona, Spain celaya@iri.upc.edu.

Neural Computation
|February 25, 2015
PubMed
Summary

This study introduces a weight-dependent forgetting method for the Expectation-Maximization (EM) algorithm, improving approximation accuracy and stability over time-dependent methods.

Area of Science:

  • Machine Learning
  • Statistical Algorithms

Background:

  • The online EM algorithm uses a time-dependent discount factor to forget old estimates.
  • Uniform forgetting can lead to excessive data loss in less frequently sampled regions.

Purpose of the Study:

  • To propose a modified EM algorithm with weight-dependent forgetting.
  • To enhance the accuracy and stability of online EM estimations.

Main Methods:

  • Developed a novel weight-dependent forgetting mechanism for the EM algorithm.
  • Compared the performance of weight-dependent vs. time-dependent forgetting strategies.

Main Results:

  • The weight-dependent approach significantly improves approximation accuracy.
  • The proposed method demonstrates substantially greater stability compared to the time-dependent approach.

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  • Addresses the issue of excessive forgetting in sparsely sampled data regions.

    • Conclusions:

    • Weight-dependent forgetting offers a superior alternative to time-dependent forgetting in online EM algorithms.
    • The modified algorithm provides more reliable and stable estimations for mixture components.