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Related Concept Videos

Surveys02:16

Surveys

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Often, psychologists develop surveys as a means of gathering data. Surveys are lists of questions to be answered by research participants, and can be delivered as paper-and-pencil questionnaires, administered electronically, or conducted verbally. Generally, the survey itself can be completed in a short time, and the ease of administering a survey makes it easy to collect data from a large number of people.
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Geographic Information Systems (GIS) operate across three levels of application, each representing an increasing degree of complexity: data management, analysis, and prediction. These levels reflect the expanding functionality and versatility of GIS technology in handling spatial data for diverse purposes.Data ManagementAt its foundational level, GIS serves as a tool for data management, enabling the input, storage, retrieval, and organization of spatial data. This level is often employed in...
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The systematic method of obtaining and analyzing accurate information of a population is called data collection. A survey is a standard method of data collection that involves collecting information from a target human population about their experience, opinion, or knowledge of a product, service, or process. The responses are recorded and interpreted. The most common survey examples are written questionnaires, face-to-face or telephonic conversations, focus groups, and electronic (e-mail or...
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Statistical Analysis System (SAS)01:14

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SAS, short for Statistical Analysis System, is a powerful data analysis, management, and visualization tool. Developed by the SAS Institute in the early 1970s, SAS has evolved into a comprehensive software suite used across various industries for statistical analysis, business intelligence, and predictive modeling.
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The term "bootstrap" originated in the 19th century as a metaphor for self-improvement or achieving something independently, without external assistance. This concept extends to statistical bootstrapping, a self-contained method for estimating population parameters through resampling, even though it can be computationally intensive. Developed by the American statistician Dr. Bradley Efron in 1979, bootstrapping provides a robust way to perform inference when the original sample size is...
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Surveys are essential for marking property boundaries near water bodies. Different types of surveys are defined, each with its own function. Land surveys mark the property boundaries, while route surveys determine the position of properties on nearby highways. Topographic surveys create maps by capturing the three-dimensional features of the land. Hydrographic surveys focus on the shapes of underwater areas and the movement of streams through the properties. Mine surveys determine the relative...
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    Self-supervised learning (SSL) reduces the need for labeled data by learning from unlabeled data. This review explores SSL algorithms, applications, trends, and open research questions in machine learning.

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    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Computer Science

    Background:

    • Deep supervised learning demands extensive labeled data, which is costly and time-consuming to acquire.
    • Self-supervised learning (SSL) offers a solution by extracting features from unlabeled data, a rapidly advancing field.
    • Existing literature lacks comprehensive reviews connecting different SSL variants and their evolution.

    Purpose of the Study:

    • To provide a comprehensive review of self-supervised learning (SSL) methods.
    • To elucidate the connections and evolution of various SSL algorithms.
    • To identify key trends and open research questions in SSL.

    Main Methods:

    • Review and analysis of diverse self-supervised learning algorithms.
    • Comparison of commonalities and differences in SSL motivations and methodologies.
    • Exploration of representative applications across image processing, computer vision, and natural language processing.

    Main Results:

    • Detailed introduction to the motivations behind SSL algorithms.
    • Comparative analysis of existing SSL methods.
    • Overview of SSL applications in key domains.
    • Discussion of three primary trends in SSL research.

    Conclusions:

    • SSL is a promising approach to overcome data labeling challenges in deep learning.
    • Understanding the landscape of SSL algorithms, applications, and trends is crucial for future research.
    • Identifying open research questions will guide the next wave of SSL advancements.