Coming to grips with malaria in the new millennium / / UN Millennium Project 2005, Task Force on HIV/AIDS, Malaria, TB, and Access to Essential Medicines, Working Group on Malaria ; Lead authors: Awash Teklehaimanot (Coordinator). [et. al.]

• Record Nr.: UNINA9910450653703321
• Autore: Teklehaimanot Awash
• Pubbl/distr/stampa: London ; ; Sterling, Va. : , : Earthscan, , 2005
• ISBN: 1-136-55063-1; 1-280-47541-2; 9786610475414; 1-4619-0573-7; 600-00-0100-2; 1-84977-349-1
• Descrizione fisica: 1 online resource (147 p.)
• Disciplina: 362.196/93620091724; 614.5320091724; 616.9362
• Soggetti: Malaria - Developing countries - Prevention; Malaria - Prevention - International cooperation; Electronic books.
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: "Achieving the Millennium Development Goals"--Cover.; Sponsored by the United Nations Development Programme on behalf of the UN Development Group.
• Nota di bibliografia: Includes bibliographical references (p. [119]-129).
• Nota di contenuto: Coming to grips with malaria inthe new millennium; Copyright; Foreword; Contents; Working group members; Preface; Acknowledgements; Abbreviations; Millennium Development Goals; Executive summary; Chapter 1 Introduction; The Millennium Development Goal and target for malaria; Organization of this report; Chapter 2 The resurgence and burden of malaria; Health burden; Economic and social burden; Chapter 3 Review of major initiatives and institutional policies for malaria control; Global Malaria Eradication Program; Global Malaria Control Strategy; Harare Declaration on Malaria Prevention and ControlMultilateral Initiative on Malaria; Roll Back Malaria Initiative; Abuja Declaration on Roll Back Malaria; Medicines for Malaria Venture; Global Fund to fight AIDS, Tuberculosis, and Malaria; Chapter 4 Malaria control strategies; Disease prevention strategies; Disease management strategies; Epidemic prevention and control strategies; Information, education, and communication strategies; Monitoring and evaluation; Chapter 5 Examples of successful scale-up of malaria control programs; Tigray region of Ethiopia; Highlands of Madagascar; Viet Nam; South AfricaTanzania; Lessons learned; Chapter 6 Priority challenges for scaling up malaria control programs; Strengthening health systems; Human resources capacity; Social mobilization of communities; Partnerships; Programmatic challenges; Chapter 7 Developing a global plan to achieve the Millennium Development Goal target for malaria; Conditions for achieving a sustained impact; Developing a global plan for reducing the burden of malaria; Components of a global plan; Needs assessment: costing and financing; Resource mobilization: needs assessment at the global level; Resource mobilization: needs assessment at the country level - EthiopiaChapter 8 Monitoring and evaluation; Monitoring and evaluation of health programs; Malaria-related Millennium Development Goal, targets, and indicators; Coverage measures; Main approaches to data collection for monitoring malaria control; Monitoring the effectiveness of antimalarials and insecticides; Developing geographic information systems and remote sensing; Cost-effectiveness of service provision; Linkage of malaria monitoring with poverty alleviation; Chapter 9 Research and development to meet current and future needsAntimalarial medicine development; Malaria diagnostics; Malaria management in young children; Malaria vector; Malaria vaccines; Chapter 10 Recommendations; 1. Establish a realistic and measurable target on malaria; 2. Enhance political commitment at country and global levels; 3. Strengthen health systems at national and district levels; 4. Develop human resources for program implementation; 5. Promote social mobilization and community participation; 6. Provide effective antimalarial supplies and commodities; 7. Apply an integrated package of interventions
• Sommario/riassunto: The Millennium Development Goals, adopted at the UN Millennium Summit in 2000, are the world's targets for dramatically reducing extreme poverty in its many dimensions by 2015?income poverty, hunger, disease, exclusion, lack of infrastructure and shelter?while promoting gender equality, education, health and environmental sustainability. These bold goals can be met in all parts of the world if nations follow through on their commitments to work together to meet them. Achieving the Millennium Development Goals offers the prospect of a more secure, just, and prosperous world for all. The UN Mi

Pattern recognition [[electronic resource] /] / Sergios Theodoridis, Konstantinos Koutroumbas

• Record Nr.: UNINA9910784636303321
• Autore: Theodoridis Sergios <1951->
• Pubbl/distr/stampa: San Diego, CA, : Academic Press, c2006
• ISBN: 1-281-31146-4; 9786611311469; 0-08-051361-1
• Edizione: [3rd ed.]
• Descrizione fisica: 1 online resource (854 p.)
• Altri autori (Persone): KoutroumbasKonstantinos <1967->
• Disciplina: 006.3; 006.4
• Soggetti: Pattern recognition systems
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Description based upon print version of record.
• Nota di contenuto: Front cover; Title page; Copyright page; Table of contents; PREFACE; 1 INTRODUCTION; 1.1 IS PATTERN RECOGNITION IMPORTANT?; 1.2 FEATURES, FEATURE VECTORS, AND CLASSIFIERS; 1.3 SUPERVISED VERSUS UNSUPERVISED PATTERN RECOGNITION; 1.4 OUTLINE OF THE BOOK; 2 CLASSIFIERS BASED ON BAYES DECISION THEORY; 2.1 INTRODUCTION; 2.2 BAYES DECISION THEORY; 2.3 DISCRIMINANT FUNCTIONS AND DECISION SURFACES; 2.4 BAYESIAN CLASSIFICATION FOR NORMAL DISTRIBUTIONS; 2.5 ESTIMATION OF UNKNOWN PROBABILITY DENSITY FUNCTIONS; 2.6 THE NEAREST NEIGHBOR RULE; 2.7 BAYESIAN NETWORKS; 3 LINEAR CLASSIFIERS; 3.1 INTRODUCTION; 3.2 LINEAR DISCRIMINANT FUNCTIONS AND DECISION HYPERPLANES3.3 THE PERCEPTRON ALGORITHM; 3.4 LEAST SQUARES METHODS; 3.5 MEAN SQUARE ESTIMATION REVISITED; 3.6 LOGISTIC DISCRIMINATION; 3.7 SUPPORT VECTOR MACHINES; 4 NONLINEAR CLASSIFIERS; 4.1 INTRODUCTION; 4.2 THE XOR PROBLEM; 4.3 THE TWO-LAYER PERCEPTRON; 4.4 THREE-LAYER PERCEPTRONS; 4.5 ALGORITHMS BASED ON EXACT CLASSIFICATION OF THE TRAINING SET; 4.6 THE BACKPROPAGATION ALGORITHM; 4.7 VARIATIONS ON THE BACKPROPAGATION THEME; 4.8 THE COST FUNCTION CHOICE; 4.9 CHOICE OF THE NETWORK SIZE; 4.10 A SIMULATION EXAMPLE; 4.11 NETWORKS WITH WEIGHT SHARING4.12 GENERALIZED LINEAR ; CLASSIFIERS; 4.13 CAPACITY OF THE l-DIMENSIONAL SPACE IN LINEAR DICHOTOMIES; 4.14 POLYNOMIAL CLASSIFIERS; 4.15 RADIAL BASIS FUNCTION NETWORKS; 4.16 UNIVERSAL APPROXIMATORS; 4.17 SUPPORT VECTOR MACHINES: THE NONLINEAR CASE; 4.18 DECISION TREES; 4.19 COMBINING CLASSIFIERS; 4.20 THE BOOSTING APPROACH TO COMBINE CLASSIFIERS; 4.21 DISCUSSION; 5 FEATURE SELECTION; 5.1 INTRODUCTION; 5.2 PREPROCESSING; 5.3 FEATURE SELECTION BASED ON STATISTICAL HYPOTHESIS TESTING; 5.4 THE RECEIVER OPERATING CHARACTERISTICS (ROC) CURVE; 5.5 CLASS SEPARABILITY MEASURES5.6 FEATURE SUBSET SELECTION; 5.7 OPTIMAL FEATURE GENERATION; 5.8 NEURAL NETWORKS AND FEATURE GENERATION/ SELECTION; 5.9 A HINT ON GENERALIZATION THEORY; 5.10 THE BAYESIAN INFORMATION CRITERION; 6 FEATURE GENERATION I: LINEAR TRANSFORMS; 6.1 INTRODUCTION; 6.2 BASIS VECTORS AND IMAGES; 6.3 THE KARHUNEN-LOÈVE TRANSFORM; 6.4 THE SINGULAR VALUE DECOMPOSITION; 6.5 INDEPENDENT COMPONENT ANALYSIS; 6.6 THE DISCRETE FOURIER TRANSFORM (DFT); 6.7 THE DISCRETE COSINE AND SINE TRANSFORMS; 6.8 THE HADAMARD TRANSFORM; 6.9 THE HAAR TRANSFORM; 6.10 THE HAAR EXPANSION REVISITED; 6.11 DISCRETE TIMEWAVELET TRANSFORM (DTWT)6.12 THE MULTIRESOLUTION INTERPRETATION; 6.13 WAVELET PACKETS; 6.14 A LOOK AT TWO-DIMENSIONAL GENERALIZATIONS; 6.15 APPLICATIONS; 7 FEATURE GENERATION II; 7.1 INTRODUCTION; 7.2 REGIONAL FEATURES; 7.3 FEATURES FOR SHAPE AND SIZE CHARACTERIZATION; 7.4 A GLIMPSE AT FRACTALS; 7.5 TYPICAL FEATURES FOR SPEECH AND AUDIO CLASSIFICATION; 8 TEMPLATE MATCHING; 8.1 INTRODUCTION; 8.2 MEASURES BASED ON OPTIMAL PATH SEARCHING TECHNIQUES; 8.3 MEASURES BASED ON CORRELATIONS; 8.4 DEFORMABLE TEMPLATE MODELS; 9 CONTEXT-DEPENDENT CLASSIFICATION; 9.1 INTRODUCTION; 9.2 THE BAYES CLASSIFIER
• Sommario/riassunto: Pattern recognition is a fast growing area with applications in a widely diverse number of fields such as communications engineering, bioinformatics, data mining, content-based database retrieval, to name but a few. This new edition addresses and keeps pace with the most recent advancements in these and related areas. This new edition: a) covers Data Mining, which was not treated in the previous edition, and is integrated with existing material in the book, b) includes new results on Learning Theory and Support Vector Machines, that are at the forefront of today's research, with a lot of inter