Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

This book discusses how to combine type-2 fuzzy sets and graphical models to solve a range of real-world pattern recognition problems such as speech recognition, handwritten Chinese character recognition, topic modeling as well as human action recognition. It covers these recent developments while also providing a comprehensive introduction to the fields of type-2 fuzzy sets and graphical models. Though primarily intended for graduate students, researchers and practitioners in fuzzy logic and pattern recognition, the book can also serve as a valuable reference work for researchers without any previous knowledge of these fields. Dr. Jia Zeng is a Professor at the School of Computer Science and Technology, Soochow University, China. Dr. Zhi-Qiang Liu is a Professor at the School of Creative Media, City University of Hong Kong, China.

Engineering. --- Computational Intelligence. --- Artificial Intelligence (incl. Robotics). --- Pattern Recognition. --- Signal, Image and Speech Processing. --- Artificial intelligence. --- Optical pattern recognition. --- Ingénierie --- Intelligence artificielle --- Reconnaissance optique des formes (Informatique) --- Engineering & Applied Sciences --- Computer Science --- Pattern recognition. --- Computational intelligence. --- Intelligence, Computational --- Artificial intelligence --- Soft computing --- Design perception --- Pattern recognition --- Form perception --- Perception --- Figure-ground perception --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Construction --- Industrial arts --- Technology --- Artificial Intelligence. --- Optical data processing --- Pattern perception --- Perceptrons --- Visual discrimination --- Pattern recognition systems --- Mathematical models. --- Signal processing. --- Image processing. --- Speech processing systems. --- Computational linguistics --- Electronic systems --- Information theory --- Modulation theory --- Oral communication --- Speech --- Telecommunication --- Singing voice synthesizers --- Pictorial data processing --- Picture processing --- Processing, Image --- Imaging systems --- Processing, Signal --- Information measurement --- Signal theory (Telecommunication)

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Zonder onderwerpscode

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

January 1999 - National and urban motor vehicle ownership increases at about the same rate as income, whereas road length increases with income mainly at the national level. So, urban congestion grows with income. Controlling vehicle fleet growth and use would require high taxes that increase faster than income - or there could be congestion tolls. Ingram and Liu survey past trends in vehicle ownership and road network expansion to analyze determinants of their growth at the national and urban level. Surprisingly, they find that: Nationally, income is a major determinant of both vehicle ownership and road length; Nationally, paved road length and vehicle ownership has been increasing about as fast as income, while total road length is increasing less rapidly than income; In urban areas vehicle ownership increases as fast as income while road length increases very slowly with income. Because national paved road networks are expanding about as fast as national motor vehicle fleets, national congestion is unlikely to be worsening. But because urban road length is growing much more slowly than the number of urban motor vehicles, urban congestion is rising with income over time. Increased urban congestion is stimulating decentralized urban growth. Income elasticities are greater than price elasticities in absolute terms, for both vehicle ownership and use - an important finding because prices are often used as an instrument to control motor vehicle ownership and use. If price elasticities are half as large as income elasticities, prices would have to grow twice as fast as incomes to stabilize vehicle ownership. Breaking the link between income growth, rising congestion, and urban decentralization will be difficult: Restraining auto ownership in urban areas requires high tax rates, and increasing the supply of urban roads is costly. Elasticity estimates vary, but a good point estimate for the income elasticity of fleet growth is 1. This means country motor vehicle fleets grow in proportion to country incomes. More than half the world's annual increase in motor vehicles is likely to occur in high-income countries until 2025 (assuming GNP growth of 3 percent in high-income countries, 5 percent in low- and middle-income countries). The motor vehicle fleet in low- and middle-income countries is not projected to exceed that in high-income countries until after 2050. Carbon dioxide emissions are likely to be distributed similarly. This paper-a joint product of the Research Advisory Staff and the Transport Division, Transport, Water, and Urban Development Department-is part of a research project on motorization and roads. The authors may be contacted at gingram@worldbank.org or zliu@worldbank.org.

Air --- Air Pollution --- Auto Dependence --- Buses --- Cars --- Congestion --- Externalities --- Motor Vehicle --- Motor Vehicle Use --- Motor Vehicles --- Road --- Road Network --- Road Provision --- Roads --- Trans Transit Use --- Transport --- Transport Economics, Policy and Planning --- Trucks --- Urban Transport --- Vehicle Ownership

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Transport. Traffic --- Environmental planning --- Economic geography