Cambria Erik (232 results)

Condition: New.

Condition: As New. Unread book in perfect condition.

Condition: As New. Unread book in perfect condition.

Paperback. Condition: new. Paperback. About half a century ago, AI pioneers like Marvin Minsky embarked on the ambitious project of emulating how the human mind encodes and decodes meaning. While today we have a better understanding of the brain thanks to neuroscience, we are still far from unlocking the secrets of the mind, especially when it comes to language, the prime example of human intelligence. Understanding natural language understanding, i.e., understanding how the mind encodes and decodes meaning through language, is a significant milestone in our journey towards creating machines that genuinely comprehend human language. Large language models (LLMs) such as GPT-4 have astounded us with their ability to generate coherent, contextually relevant text, seemingly bridging the gap between human and machine communication. Yet, despite their impressive capabilities, these models operate on statistical patterns rather than true comprehension. This textbook delves into the nuanced differences between these two paradigms and explores the future of AI as we strive to achieve true natural language understanding (NLU). LLMs excel at identifying and replicating patterns within vast datasets, producing responses that appear intelligent and meaningful. They can generate text that mimics human writing styles, provide summaries of complex documents, and even engage in extended dialogues with users. However, their limitations become evident when they encounter tasks that require deeper understanding, reasoning, and contextual knowledge. An NLU system that deconstructs meaning leveraging linguistics and semiotics (on top of statistical analysis) represents a more profound level of language comprehension. It involves understanding context in a manner similar to human cognition, discerning subtle meanings, implications, and nuances that current LLMs might miss or misinterpret. NLU grasps the semantics behind words and sentences, comprehending synonyms, metaphors, idioms, and abstract concepts with precision.This textbook explores the current state of LLMs, their capabilities and limitations, and contrasts them with the aspirational goals of NLU. The author delves into the technical foundations required for achieving true NLU, including advanced knowledge representation, hybrid AI systems, and neurosymbolic integration, while also examining the ethical implications and societal impacts of developing AI systems that genuinely understand human language. Containing exercises, a final assignment and a comprehensive quiz, the textbook is meant as a reference for courses on information retrieval, AI, NLP, data analytics, data mining and more. Understanding natural language understanding, i.e., understanding how the mind encodes and decodes meaning through language, is a significant milestone in our journey towards creating machines that genuinely comprehend human language. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Condition: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.

Condition: New.

Paperback. Condition: New.

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Condition: Very Good. 2015 Springer International (Cham, Switzerland), 6 1/2 x 9 1/2 inches tall pictorial hardcover, no dust jacket (as issued), illustrated with black-and-white photographs, charts and graphs, viii, 400 pp. Slight bumping to edges of covers. Otherwise, a very good to near fine copy - clean, bright and unmarked. Due to the weight of the book, additional postage will be required for standard international orders. ~SP05~ [2.5P] contains some selected papers from the International Conference on Extreme Learning Machine 2014, which was held in Singapore, December 8-10, 2014. This conference brought together the researchers and practitioners of Extreme Learning Machine (ELM) from a variety of fields to promote research and development of "learning without iterative tuning". The book covers theories, algorithms and applications of ELM. Contents: Using Extreme Learning Machine for Filamentous Bulking Prediction and Forecast in Wastewater Treatment Plants; Extreme Learning Machine for Linear Dynamical Systems Classification: Application to Human Activity Recognition; Lens Distortion Correction Using ELM; Pedestrian Detection in Thermal Infrared Image using Extreme Learning Machine; Dynamic Texture Video Classification Using Extreme Learning Machine; Uncertain XML Documents Classification Using Extreme Learning Machine; Encrypted traffic identification based on randomness sparse feature and extreme learning machine; Network Intrusion Detection Based on Extreme Learning Machine; A Study on Three-dimensional Motion History Image and Extreme Learning Machine Oriented Body Movements Trajectory Recognition; An Improved ELM Algorithm for the Measurement of Hot Metal Temperature in Blast Furnace; Wi-Fi and Motion Sensors based Indoor Localization Combining ELM and Particle Filter; Online Sequential Extreme Learning Machine for Watermarking; Adaptive neural control of quadrotor helicopter with extreme learning machine; Keyword Search on Probabilistic XML Data based on ELM; A Novel HVS Based Gray Scale Image Watermarking Scheme Using Fast Fuzzy; ELM Hybrid Architecture; Wearable EyeGlass based Fall Detection using Weighted ELM; Concise Feature Extraction based ELM for Active Service Quality Prediction; Multi-class AdaBoost ELM and Its Application in LBP Based Face Recognition; Detecting Copy Directions among Programs Using Extreme Learning Machines; Extreme learning machine for reservoir parameter estimation in heterogeneous reservoir; Multifault Diagnosis for Rolling Element Bearings Based on Extreme Learning Machine; Gradient-based No-Reference Image Blur Assessment Using Extreme Learning Machine; RFID Enabled Indoor Positioning for Real-time Manufacturing Execution System based on OS-ELM; An Online Sequential Extreme Learning Machine for Tidal Prediction based on Improved Gath-Geva Fuzzy Segmentation; Recognition of Human Stair Ascent and Descent Activities based on Extreme Learning Machine; ELM Based Dynamic Modeling for Online Prediction of Content in Molten Iron; Distributed Learning over Massive XML Documents in ELM Feature Space; Hyperspectral Image Nonlinear Unmixing by Ensemble ELM Regression; Text-Image Separation and Indexing in Historic Patent Document Image Based on Extreme Learning Machine; Anomaly Detection with ELM-based Visual Attribute and Spatio-temporal Pyramid; Modelling and Prediction of Surface Roughness and Power Consumption using Parallel Extreme Learning Machine based Particle Swarm Optimization; OS-ELM based Emotion Recognition for Empathetic Elderly Companion; Access Behavior Prediction in Distributed StorageSystem using Regularized Extreme Learning Machine; ELM Based Fast CFD Model with Sensor Adjustment; Melasma Image Segmentation Using Extreme Learning Machine; Detection of Drivers' Distraction Using Semi-Supervised Extreme Learning Machine; Driver Workload Detection in On-road Driving Environment using Machine Learning.…

Condition: New.

Condition: New.

Condition: New.

Condition: New. In English.

Condition: New. In English.

Condition: New.

Paperback or Softback. Condition: New. Sentic Computing: Techniques, Tools, and Applications. Book.

Condition: As New. Unread book in perfect condition.

Condition: New. pp. 172.

Paperback. Condition: Brand New. 1st edition. 153 pages. 9.00x6.50x0.50 inches. In Stock.

Paperback. Condition: Brand New. 2014 edition. 224 pages. 9.00x6.25x0.75 inches. In Stock.

Condition: Fine. 203 pp., hardcover, previous owner's name neatly inked to the title page, else fine. - If you are reading this, this item is actually (physically) in our stock and ready for shipment once ordered. We are not bookjackers. Buyer is responsible for any additional duties, taxes, or fees required by recipient's country. Photos available upon request.…

Paperback. Condition: new. Paperback. About half a century ago, AI pioneers like Marvin Minsky embarked on the ambitious project of emulating how the human mind encodes and decodes meaning. While today we have a better understanding of the brain thanks to neuroscience, we are still far from unlocking the secrets of the mind, especially when it comes to language, the prime example of human intelligence. Understanding natural language understanding, i.e., understanding how the mind encodes and decodes meaning through language, is a significant milestone in our journey towards creating machines that genuinely comprehend human language. Large language models (LLMs) such as GPT-4 have astounded us with their ability to generate coherent, contextually relevant text, seemingly bridging the gap between human and machine communication. Yet, despite their impressive capabilities, these models operate on statistical patterns rather than true comprehension. This textbook delves into the nuanced differences between these two paradigms and explores the future of AI as we strive to achieve true natural language understanding (NLU). LLMs excel at identifying and replicating patterns within vast datasets, producing responses that appear intelligent and meaningful. They can generate text that mimics human writing styles, provide summaries of complex documents, and even engage in extended dialogues with users. However, their limitations become evident when they encounter tasks that require deeper understanding, reasoning, and contextual knowledge. An NLU system that deconstructs meaning leveraging linguistics and semiotics (on top of statistical analysis) represents a more profound level of language comprehension. It involves understanding context in a manner similar to human cognition, discerning subtle meanings, implications, and nuances that current LLMs might miss or misinterpret. NLU grasps the semantics behind words and sentences, comprehending synonyms, metaphors, idioms, and abstract concepts with precision.This textbook explores the current state of LLMs, their capabilities and limitations, and contrasts them with the aspirational goals of NLU. The author delves into the technical foundations required for achieving true NLU, including advanced knowledge representation, hybrid AI systems, and neurosymbolic integration, while also examining the ethical implications and societal impacts of developing AI systems that genuinely understand human language. Containing exercises, a final assignment and a comprehensive quiz, the textbook is meant as a reference for courses on information retrieval, AI, NLP, data analytics, data mining and more. Understanding natural language understanding, i.e., understanding how the mind encodes and decodes meaning through language, is a significant milestone in our journey towards creating machines that genuinely comprehend human language. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - About half a century ago, AI pioneers like Marvin Minsky embarked on the ambitious project of emulating how the human mind encodes and decodes meaning. While today we have a better understanding of the brain thanks to neuroscience, we are still far from unlocking the secrets of the mind, especially when it comes to language, the prime example of human intelligence. 'Understanding natural language understanding', i.e., understanding how the mind encodes and decodes meaning through language, is a significant milestone in our journey towards creating machines that genuinely comprehend human language. Large language models (LLMs) such as GPT-4 have astounded us with their ability to generate coherent, contextually relevant text, seemingly bridging the gap between human and machine communication. Yet, despite their impressive capabilities, these models operate on statistical patterns rather than true comprehension.This textbook delves into the nuanced differences between these two paradigms and explores the future of AI as we strive to achieve true natural language understanding (NLU). LLMs excel at identifying and replicating patterns within vast datasets, producing responses that appear intelligent and meaningful. They can generate text that mimics human writing styles, provide summaries of complex documents, and even engage in extended dialogues with users. However, their limitations become evident when they encounter tasks that require deeper understanding, reasoning, and contextual knowledge. An NLU system that deconstructs meaning leveraging linguistics and semiotics (on top of statistical analysis) represents a more profound level of language comprehension. It involves understanding context in a manner similar to human cognition, discerning subtle meanings, implications, and nuances that current LLMs might miss or misinterpret. NLU grasps the semantics behind words and sentences, comprehending synonyms, metaphors, idioms, and abstract concepts with precision.This textbook explores the current state of LLMs, their capabilities and limitations, and contrasts them with the aspirational goals of NLU. The author delves into the technical foundations required for achieving true NLU, including advanced knowledge representation, hybrid AI systems, and neurosymbolic integration, while also examining the ethical implications and societal impacts of developing AI systems that genuinely understand human language. Containing exercises, a final assignment and a comprehensive quiz, the textbook is meant as a reference for courses on information retrieval, AI, NLP, data analytics, data mining and more.…

Condition: New.

Condition: New.

Paperback. Condition: new. Paperback. About half a century ago, AI pioneers like Marvin Minsky embarked on the ambitious project of emulating how the human mind encodes and decodes meaning. While today we have a better understanding of the brain thanks to neuroscience, we are still far from unlocking the secrets of the mind, especially when it comes to language, the prime example of human intelligence. Understanding natural language understanding, i.e., understanding how the mind encodes and decodes meaning through language, is a significant milestone in our journey towards creating machines that genuinely comprehend human language. Large language models (LLMs) such as GPT-4 have astounded us with their ability to generate coherent, contextually relevant text, seemingly bridging the gap between human and machine communication. Yet, despite their impressive capabilities, these models operate on statistical patterns rather than true comprehension. This textbook delves into the nuanced differences between these two paradigms and explores the future of AI as we strive to achieve true natural language understanding (NLU). LLMs excel at identifying and replicating patterns within vast datasets, producing responses that appear intelligent and meaningful. They can generate text that mimics human writing styles, provide summaries of complex documents, and even engage in extended dialogues with users. However, their limitations become evident when they encounter tasks that require deeper understanding, reasoning, and contextual knowledge. An NLU system that deconstructs meaning leveraging linguistics and semiotics (on top of statistical analysis) represents a more profound level of language comprehension. It involves understanding context in a manner similar to human cognition, discerning subtle meanings, implications, and nuances that current LLMs might miss or misinterpret. NLU grasps the semantics behind words and sentences, comprehending synonyms, metaphors, idioms, and abstract concepts with precision.This textbook explores the current state of LLMs, their capabilities and limitations, and contrasts them with the aspirational goals of NLU. The author delves into the technical foundations required for achieving true NLU, including advanced knowledge representation, hybrid AI systems, and neurosymbolic integration, while also examining the ethical implications and societal impacts of developing AI systems that genuinely understand human language. Containing exercises, a final assignment and a comprehensive quiz, the textbook is meant as a reference for courses on information retrieval, AI, NLP, data analytics, data mining and more. Understanding natural language understanding, i.e., understanding how the mind encodes and decodes meaning through language, is a significant milestone in our journey towards creating machines that genuinely comprehend human language. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…

Condition: As New. Unread book in perfect condition.

Condition: New. A brand new book in pristine condition. Showing zero signs of shelf wear, creases, or damage.

Taschenbuch. Condition: Neu. Sentic Computing | Techniques, Tools, and Applications | Erik Cambria (u. a.) | Taschenbuch | SpringerBriefs in Cognitive Computation | xviii | Englisch | 2012 | Springer | EAN 9789400750692 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. …

Taschenbuch. Condition: Neu. Semantic Web Evaluation Challenge | SemWebEval 2014 at ESWC 2014, Anissaras, Crete, Greece, May 25-29, 2014, Revised Selected Papers | Valentina Presutti (u. a.) | Taschenbuch | Communications in Computer and Information Science | xv | Englisch | 2014 | Springer | EAN 9783319120232 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.…