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Related Concept Videos

Purposive Learning01:22

Purposive Learning

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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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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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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
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Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
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Related Experiment Videos

A Maximum Entropy Framework for Semisupervised and Active Learning With Unknown and Label-Scarce Classes.

Zhicong Qiu, David J Miller, George Kesidis

    IEEE Transactions on Neural Networks and Learning Systems
    |February 2, 2016
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel semisupervised learning and active learning approach for domains with label-scarce and unknown categories. The method effectively identifies unknown classes and discriminates rare classes using maximum entropy to avoid overtraining.

    Related Experiment Videos

    Area of Science:

    • Machine Learning
    • Artificial Intelligence
    • Data Science

    Background:

    • Addressing challenges in domains with label-scarce (LS) and unknown categories, where labeled examples are minimal or absent.
    • Identifying critical features and mitigating overtraining are key issues in such learning scenarios.

    Purpose of the Study:

    • To develop a semisupervised learning (SL) and pool-based active learning (AL) classifier for domains with label-scarce and unknown categories.
    • To propose a novel objective function that preserves decision uncertainty (maximum entropy) to prevent overtraining.

    Main Methods:

    • Utilizing negative log p-values of raw features as derived inputs to a hierarchical class posterior, accommodating multiple common, LS, and unknown classes.
    • Implementing a novel semisupervised objective customized for LS/unknown category scenarios, prioritizing maximum entropy over uncertainty minimization.
    • Developing an active learning system with a unique sample-selection scheme for discovering unknown classes and discriminating LS classes.

    Main Results:

    • The proposed method, using p-value features with weight constraints, yields sparse solutions and significantly outperforms using raw features.
    • Experiments on UCI Machine Learning domains demonstrate the effectiveness of preserving decision uncertainty (maxEnt) in LS SL and AL, especially in early AL stages.
    • The active learning system successfully discovers unknown classes and differentiates LS classes with minimal oracle labeling.

    Conclusions:

    • The developed semisupervised and active learning framework is effective for handling label-scarce and unknown categories in machine learning.
    • Preserving decision uncertainty via maximum entropy is crucial for robust learning in LS/unknown category scenarios, preventing overtraining.
    • The proposed active learning strategy efficiently identifies and classifies unknown and label-scarce categories, reducing the need for extensive manual labeling.