Walter j scheirer (41 resultados)

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Paperback. Condición: New. 2025 Catholic Media Association Second Place Award, Catholic Social Teaching2025 Catholic Media Association Second Place Award, Pastoral Ministry - Parish LifeExplore new modes of creation to bring virtue back into virtual spaces. At its best, the internet channels the world into a global village of sorts, where digital citizens learn from each other, explore new modes of creation, and help others work through dilemmas in both physical and virtual spaces. Virtue in Virtual Spaces argues that the internet doesn't have to be the cultural wasteland of click-bait, partisan politics, and vulgar content that we see too often today.Technology has tremendous potential for good because of the inherent goodness of human creation and creativity which can be achieved through the development and use of technology. The authors draw from writing on virtue ethics and Catholic Social Teaching to demonstrate this potential goodness of technology. Eight of the main themes of Catholic Social Teaching are used to build a framework for designing technology to promote human flourishing. In this book, readers will engage with the philosophies behind their favorite social media platforms, examine how the design features in these platforms shape habits and imagination, and gain dialogue-based skills to bring virtue back into virtual spaces.…

hardcover. Condición: Near Fine. Condition Notes: Excellent, unmarked copy with little wear and tight binding. We ship in recyclable American-made mailers. 100% money-back guarantee on all orders.

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Paperback. Condición: new. Paperback. A common feature of many approaches to modeling sensory statistics is an emphasis on capturing the "average." From early representations in the brain, to highly abstracted class categories in machine learning for classification tasks, central-tendency models based on the Gaussian distribution are a seemingly natural and obvious choice for modeling sensory data. However, insights from neuroscience, psychology, and computer vision suggest an alternate strategy: preferentially focusing representational resources on the extremes of the distribution of sensory inputs. The notion of treating extrema near a decision boundary as features is not necessarily new, but a comprehensive statistical theory of recognition based on extrema is only now just emerging in the computer vision literature. This book begins by introducing the statistical Extreme Value Theory (EVT) for visual recognition. In contrast to central-tendency modeling, it is hypothesized that distributions near decision boundaries form a more powerful model for recognition tasks by focusing coding resources on data that are arguably the most diagnostic features. EVT has several important properties: strong statistical grounding, better modeling accuracy near decision boundaries than Gaussian modeling, the ability to model asymmetric decision boundaries, and accurate prediction of the probability of an event beyond our experience. The second part of the book uses the theory to describe a new class of machine learning algorithms for decision making that are a measurable advance beyond the state-of-the-art. This includes methods for post-recognition score analysis, information fusion, multi-attribute spaces, and calibration of supervised machine learning algorithms. EVT has several important properties: strong statistical grounding, better modeling accuracy near decision boundaries than Gaussian modeling, the ability to model asymmetric decision boundaries, and accurate prediction of the probability of an event beyond our experience. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

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Condición: As New. Unread book in perfect condition.

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Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - A common feature of many approaches to modeling sensory statistics is an emphasis on capturing the 'average.' From early representations in the brain, to highly abstracted class categories in machine learning for classification tasks, central-tendency models based on the Gaussian distribution are a seemingly natural and obvious choice for modeling sensory data. However, insights from neuroscience, psychology, and computer vision suggest an alternate strategy: preferentially focusing representational resources on the extremes of the distribution of sensory inputs. The notion of treating extrema near a decision boundary as features is not necessarily new, but a comprehensive statistical theory of recognition based on extrema is only now just emerging in the computer vision literature. This book begins by introducing the statistical Extreme Value Theory (EVT) for visual recognition. In contrast to central-tendency modeling, it is hypothesized that distributions near decision boundaries form a more powerful model for recognition tasks by focusing coding resources on data that are arguably the most diagnostic features. EVT has several important properties: strong statistical grounding, better modeling accuracy near decision boundaries than Gaussian modeling, the ability to model asymmetric decision boundaries, and accurate prediction of the probability of an event beyond our experience. The second part of the book uses the theory to describe a new class of machine learning algorithms for decision making that are a measurable advance beyond the state-of-the-art. This includes methods for post-recognition score analysis, information fusion, multi-attribute spaces, and calibration of supervised machine learning algorithms.…

Paperback. Condición: New. 2025 Catholic Media Association Second Place Award, Catholic Social Teaching2025 Catholic Media Association Second Place Award, Pastoral Ministry - Parish LifeExplore new modes of creation to bring virtue back into virtual spaces. At its best, the internet channels the world into a global village of sorts, where digital citizens learn from each other, explore new modes of creation, and help others work through dilemmas in both physical and virtual spaces. Virtue in Virtual Spaces argues that the internet doesn't have to be the cultural wasteland of click-bait, partisan politics, and vulgar content that we see too often today.Technology has tremendous potential for good because of the inherent goodness of human creation and creativity which can be achieved through the development and use of technology. The authors draw from writing on virtue ethics and Catholic Social Teaching to demonstrate this potential goodness of technology. Eight of the main themes of Catholic Social Teaching are used to build a framework for designing technology to promote human flourishing. In this book, readers will engage with the philosophies behind their favorite social media platforms, examine how the design features in these platforms shape habits and imagination, and gain dialogue-based skills to bring virtue back into virtual spaces.…

Paperback. Condición: new. Paperback. A common feature of many approaches to modeling sensory statistics is an emphasis on capturing the "average." From early representations in the brain, to highly abstracted class categories in machine learning for classification tasks, central-tendency models based on the Gaussian distribution are a seemingly natural and obvious choice for modeling sensory data. However, insights from neuroscience, psychology, and computer vision suggest an alternate strategy: preferentially focusing representational resources on the extremes of the distribution of sensory inputs. The notion of treating extrema near a decision boundary as features is not necessarily new, but a comprehensive statistical theory of recognition based on extrema is only now just emerging in the computer vision literature. This book begins by introducing the statistical Extreme Value Theory (EVT) for visual recognition. In contrast to central-tendency modeling, it is hypothesized that distributions near decision boundaries form a more powerful model for recognition tasks by focusing coding resources on data that are arguably the most diagnostic features. EVT has several important properties: strong statistical grounding, better modeling accuracy near decision boundaries than Gaussian modeling, the ability to model asymmetric decision boundaries, and accurate prediction of the probability of an event beyond our experience. The second part of the book uses the theory to describe a new class of machine learning algorithms for decision making that are a measurable advance beyond the state-of-the-art. This includes methods for post-recognition score analysis, information fusion, multi-attribute spaces, and calibration of supervised machine learning algorithms. EVT has several important properties: strong statistical grounding, better modeling accuracy near decision boundaries than Gaussian modeling, the ability to model asymmetric decision boundaries, and accurate prediction of the probability of an event beyond our experience. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…

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Hardcover. Condición: Brand New. 189 pages. 9.25x6.25x0.75 inches. In Stock.

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