Tabla de Contenidos: “…Cosine distancePearson correlation; Dimensionality reduction; Principal component analysis; Data mining techniques; Cluster analysis; Explaining the k-means cluster algorithm; Support vector machine; Decision trees; Ensemble methods; Bagging; Random forests; Boosting; Evaluating data-mining algorithms; Summary; Chapter 3: Recommender Systems; R package for recommendation - recommenderlab; Datasets; Jester5k, MSWeb, and MovieLense; The class for rating matrices; Computing the similarity matrix; Recommendation models; Data exploration; Exploring the nature of the data…”

Tabla de Contenidos: “…Temperature error compensation for fiber optic gyroscope based on CPSO-BP neural networkA hybrid method using aerial photograph to extract forest land area; Topology relations based on the integrative data model of vector and raster; Virtual machine migration strategy based on multi-objective optimization; Band selection for hyperspectral image based on differential evolution algorithm; Demeter satellite electric field signal mutation point detecting based on wavelet transform…”

Tabla de Contenidos: “…Query expressionsCreating sequences from collections; Usage considerations for sequences; Summary; Chapter 5: Let's Stack Up; Let's build a stack; Stack with concurrency support; Testing the stack; Algorithm - parenthesis matching using stacks; Summary; Chapter 6: See the Forest for the Trees; Tree as a data structure; The binary search tree; Navigating the tree; Abstract syntax trees; Summary; Chapter 7: Jumping the Queue; Let's make a functional queue; The FSharpx.Collections library; The MailboxProcessor class in F#; Summary; Chapter 8: Quick Boost with Graph; Graphs…”

Tabla de Contenidos: “…APPENDIX B KNOWLEDGE JAM TEMPLATESAPPENDIX C GLOSSARY OF TERMS; APPENDIX D CASE STUDIES; CASE STUDY: INSTITUTE FOR HEALTHCARE IMPROVEMENT (HEALTHCARE QUALITY IMPROVEMENT NON-PROFIT); CASE STUDY: FOREST BIOPRODUCTS RESEARCH INITIATIVE (NEW ENERGY INSTITUTE); APPENDIX E KNOWLEDGE JAM PRACTICE FAQS; WHAT IS BETTER: AN OUTSIDE OR INSIDE FACILITATOR?…”

Tabla de Contenidos: “…-- Subtopic B: Navigating the Platform -- Introducing Jupyter Notebooks -- Subtopic C: Jupyter Features -- Explore some of Jupyter's most useful features -- Converting a Jupyter Notebook to a Python Script -- Subtopic D: Python Libraries -- Import the external libraries and set up the plotting environment -- Our First Analysis - The Boston Housing Dataset -- Subtopic A: Loading the Data into Jupyter Using a Pandas DataFrame -- Load the Boston housing dataset -- Subtopic B: Data Exploration -- Explore the Boston housing dataset -- Subtopic C: Introduction to Predictive Analytics with Jupyter Notebooks -- Linear models with Seaborn and scikit-learn -- Activity B: Building a Third-Order Polynomial Model -- Subtopic D: Using Categorical Features for Segmentation Analysis -- Create categorical fields from continuous variables and make segmented visualizations -- Summary -- Data Cleaning and Advanced Machine Learning -- Preparing to Train a Predictive Model -- Subtopic A: Determining a Plan for Predictive Analytics -- Subtopic B: Preprocessing Data for Machine Learning -- Explore data preprocessing tools and methods -- Activity A: Preparing to Train a Predictive Model for the Employee-Retention Problem -- Training Classification Models -- Subtopic A: Introduction to Classification Algorithms -- Training two-feature classification models with scikit-learn -- The plot_decision_regions Function -- Training k-nearest neighbors for our model -- Training a Random Forest -- Subtopic B: Assessing Models with k-Fold Cross-Validation and Validation Curves -- Using k-fold cross validation and validation curves in Python with scikit-learn -- Subtopic C: Dimensionality Reduction Techniques…”

Tabla de Contenidos: “…Other dimensionality reduction techniques -- Linear-discriminant analysis -- Singular value decomposition -- Supervised learning -- k-nearest neighbors -- Classification -- Regression -- Support vector machines -- Decision trees -- Ensembling -- Bagging and random forests -- Boosting -- Evaluation metrics -- Accuracy -- Root mean square error -- Binary classification metrics -- Precision -- Recall -- F1 score -- Summary -- Chapter 6: Building Collaborative Filters -- Technical requirements -- The framework -- The MovieLens dataset -- Downloading the dataset -- Exploring the data -- Training and test data -- Evaluation -- User-based collaborative filtering -- Mean -- Weighted mean -- User demographics -- Item-based collaborative filtering -- Model-based approaches -- Clustering -- Supervised learning and dimensionality reduction -- Singular-value decomposition -- Summary -- Chapter 7: Hybrid Recommenders -- Technical requirements -- Introduction -- Case study - Building a hybrid model -- Summary -- Other Books You May Enjoy -- Index…”

Tabla de Contenidos: “…Persello -- Marine oil slick detection using synthetic aperture radar remote sensing techniques / Vaishali Chaudhary and Shashi Kumar -- Spaceborne C-band PolSAR backscatter and PolInSAR coherence-based modelling for forest aboveground biomass estimation / Ritwika Mukhopadhyay, Shashi Kumar, Hossein Aghababaei and Anurag Kulshrestha -- Analysis of polarimetric techniques for characterization of glacial feature / Shashwati Singh, Shashi Kumar, Praveen K. …”

Tabla de Contenidos: “…Detached houses -- Kim House, Ikuno, Osaka 1986-87 -- House in Nipponbashi, Naniwa, Osaka 1990-92 -- House in Shimogamo, Sakyo, Kyoto 1992-94 -- House in Higashi-Osaka, Osaka 1995-97 -- House in Suzaku, Nara 1996-98 -- House in Kurakuen I, Nishinomiya, Hyogo 1996-98 -- House in Kurakuen II, Nishinomiya, Hyoho 1996-2001 -- House in Fukaya, Saitama 1999-2001 -- House in Sakai, Osaka 2001-02 -- House in Wakayama, Wakayama 2001-02 -- Hu-tong House, Western Japan 2001-02 -- House in Higashi-Otsu, Shiga 2001-03 -- Residence annex for K House, Aichi 2002-03 -- Standard house 2004, Sakyo, Kyoto 2003-04 -- House in Yoyogi-Uehara, Tokyo 2003- -- Urban projects -- Auto lab car showroom, Ukyo, Kyoto 1989 -- Kyoto-Kagaku research institute, Kizu, Kyoto 1987-90 -- Yunoka footbridge, Ashikita, Kumamoto 1989-91 -- Sonobe SD office building, Sonobe, Kyoto 1991-93 -- Max Mara headquarters competition project, Reggion Emilia 1994-95 -- Murasakino Wakuden restaurant, Kita, Kyoto 1994-95 -- Competition project for the national library Kansai division, Seika, Kyoto 1996 -- Yamaguchi memorial hall, Ube, Yamaguchi 1994-97 -- Kazurasei antique gallery, Nagayo, Kyoto 1998-2000 -- Urban project of the Teresitas beachfront, Santa Cruz de Tenerife, Canary Islands 2000 -- Stadium 600 Pachinki arcade, Nagoya, Aichi 2000-01 -- Competition project for the Eda housing complex, Yokohama, Kanagawa 2002 -- Competition project for the Nam June Paik museum, Kyonggi, South Korea 2003 -- Meridian line Akashi ferry terminal, Akashi, Hyogo 2002-03 -- Competition project for the Kumano forest information center, Owase, Mie 2003 -- Sumida dental clinic, Amagasaki, Hyogo 2002-04 -- Paju SW office, Seoul, South Korea 2001-04 -- Gifu Kitakata housing, Gifu 2002- -- LT Aoyama building, Minato, Tokyo 2003- -- Installations and design -- Via bus stop chain of Garment stores, Shinjuku, Tokyo 1997 / Otaru, Hokkaido 1998-99 / Kakata, Fukuoka 1998-99 -- Interior of the iCB, Paris 1998-99 -- Interior of Room 435, Atami, Shizuoka 2002 -- Interiors of the Ono store/Kyoto, Nakagyo, Kyoto 2003 -- Aqua cube furniture, Cassina 2002-04…”

“…La repelencia al agua en suelos tiene múltiples causas, propiedades y manifestaciones; su origen está en la acumulación de compuestos hidrofóbicos orgánicos que reducen la infiltración y la disponibilidad de agua para las plantas, incrementan la escorrentía y la susceptibilidad a la erosión, y deterioran la estructura del suelo, generando pérdidas en la producción agropecuaria y forestal. En el presente libro se reúnen los resultados de la investigación que sobre este fenómeno se ha desarrollado, por más de una década, en los Andisoles de la cuenca hidrográfica de la quebrada Piedras Blancas (Antioquia). …”
Acceso restringido con credenciales UPSA

Tabla de Contenidos: “…EQUIPO INTEGRADO PARA EL APROVECHAMIENTO (...) -- 41. PLANTACIÓN FORESTAL DE 2 AÑOS. ESTABLECIDA (...) -- 42. VIVERO FAMILIAR. …”

Tabla de Contenidos: “…Cover -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- Part 1: Fundamentals of AI-Based Smart Manufacturing -- Chapter 1: Machine Learning Fundamentals -- 1.1 Introduction -- 1.2 Classification -- 1.2.1 Linear Model -- 1.2.1.1 Logistic Regression -- 1.2.1.2 Support Vector Machine -- 1.2.2 Nonlinear Model -- 1.2.2.1 K-Nearest Neighbor -- 1.2.2.2 Naive Bayes -- 1.2.2.3 Decision Tree -- 1.2.2.4 Random Forest -- 1.3 Regression -- 1.3.1 Linear Regression -- 1.3.2 Multiple Linear Regression -- 1.3.3 Logistic Regression -- 1.3.3.1 Three Types of Logistic Regression -- 1.3.4 Polynomial Regression -- 1.3.5 Support Vector Regression -- 1.3.6 Decision Tree Regression -- 1.3.7 Random Forest Regression -- 1.3.7.1 Random Forest Algorithm in Practice -- 1.3.7.2 Random Forest Algorithm Application Examples -- 1.3.8 Lasso Regression -- 1.4 Clustering -- 1.4.1 Clustering Algorithms -- 1.4.2 K-Means Clustering Algorithm -- 1.4.3 Mean Shift Clustering Algorithm -- 1.4.4 DBSCAN Clustering -- 1.4.4.1 Two Elements Required by DBSCAN -- 1.4.4.2 Steps followed to do DBSCAN Algorithm -- 1.5 Conclusion -- References -- Chapter 2: Industry 4.0 in Manufacturing, Communication, Transportation, Healthcare -- 2.1 Introduction -- 2.1.1 The Significance of Industry 4.0 Across Multiple Domains -- 2.2 Industry 4.0 in Manufacturing: Overview -- 2.2.1 Benefits of Industry 4.0 in Manufacturing -- 2.2.2 Examples of Industry 4.0 in Manufacturing -- 2.2.3 Challenges in Implementation of Industry 4.0 in Manufacturing -- 2.3 Industry 4.0 in Communication: Overview -- 2.3.1 Benefits of Industry 4.0 in Communication -- 2.3.2 Examples of Industry 4.0 in Communication -- 2.3.3 Challenges of Implementing Industry 4.0 in Communication -- 2.4 Industry 4.0 in Transportation: Overview -- 2.4.1 Industry 4.0â€TMs Advantages for Transportation…”

Tabla de Contenidos: “…Chapter 7 Understanding Active Directory -- Verifying the Filesystem -- Resilient File System (ReFS) -- NTFS -- Verifying Network Connectivity -- Basic Connectivity Tests -- Tools and Techniques for Testing Network Configuration -- Understanding Active Directory -- Domains -- Trees -- Forests -- Understanding Domain and Forest Functionality -- About the Domain Functional Level -- About Forest Functionality -- Planning the Domain Structure -- Installing Active Directory -- Improved Active Directory Features -- Read-Only Domain Controllers -- Adprep -- Active Directory Prerequisites -- The Installation Process -- Installing Additional Domain Controllers by Using Install from Media -- Verifying Active Directory Installation -- Using Event Viewer -- Using Active Directory Administrative Tools -- Testing from Clients -- Creating and Configuring Application Data Partitions -- Creating Application Data Partitions -- Managing Replicas -- Removing Replicas -- Using ntdsutil to Manage Application Data Partitions -- Configuring DNS Integration with Active Directory -- Exercise 7.7 -- Active Directory Object Overview -- Understanding Security Principals -- An Overview of OUs -- The Purpose of OUs -- Benefits of OUs -- Planning the OU Structure -- Logical Grouping of Resources -- Understanding OU Inheritance -- Delegating Administrative Control -- Applying Group Policies -- Creating OUs -- Managing OUs -- Moving, Deleting, and Renaming OUs -- Administering Properties of OUs -- Delegating Control of OUs -- Creating and Managing Active Directory Objects -- Overview of Active Directory Objects -- Managing Object Properties -- Understanding Groups -- Filtering and Advanced Active Directory Features -- Moving, Renaming, and Deleting Active Directory Objects -- Resetting an Existing Computer Account -- Understanding Dynamic Access Control…”

Tabla de Contenidos: “…. -- Generating regression trees for data with categorical predictors -- Generating regression trees using the ensemble method - Bagging and Boosting -- See also -- Building random forest models for regression -- Getting ready -- How to do it... -- How it works... -- There's more... -- Controlling forest generation -- See also -- Using neural networks for regression -- Getting ready -- How to do it... -- How it works... -- See also -- Performing k-fold cross-validation -- Getting ready -- How to do it... -- How it works... -- See also -- Performing leave-one-out cross-validation to limit overfitting -- How to do it…”

Tabla de Contenidos: “….); It Takes a Global Village: Distributed Cognition and the Digital-Age Classroom; Educommunication in distance education: e-mentor communication processes for dialogue development; Communication Processes at the "Distance Learning in the Amazon Forest Project"; Learning in Digital Media; the Legacy of McLuhan and his Impact on Formal Education; Educational aspects of the use of internet and mobile phones by migrants in Barcelona and Ciudad Juárez…”

Tabla de Contenidos: “…Criterios de elección Características comparadas ; Conclusión; Aplicaciones de la neumática ; Introducción; Aplicaciones de la neumática en distintos procesos industrales ; Agricultura y explotación forestal; Industria de producción de energía ; Industria química y petrolífera ; Industria del plástico ; Industria metalúrgica ; Industria de la madera. …”

Tabla de Contenidos: “…Co-operation in small forest producers in Bolivia / Jorge Téllez Carrasco and José Blanes Jiménez -- 16. …”

Tabla de Contenidos: “…/ Pio Caroni -- El derecho municipal medieval de la Cataluña Nueva (siglos XIII-XVII) / Josep Serrano Daura -- Algunas consideraciones sobre el régimen local cantábrico frente al fomento naval y forestal en la Corona de Castilla (c.1560-1570) / Alfredo José Martínez González -- Organização judiciária e administração da justiça no Portugal filipino: a reformaçam da justiça de Filipe I de Portugal (1582) / Jorge Veiga Testos -- El oficio de secretario en la doctrina jurídica de la Edad Moderna / Marina Rojo Gallego-Burín -- Notas sobre la reforma municipal carolina en Sevilla: la representación del común / María del Mar Tizón Ferrer -- Un programa ilustrado para la formación de juristas. …”

Tabla de Contenidos: “…Distributed Data Mining and Mining Multi-Agent DataData Mining Process-Related Problems; Security, Privacy, and Data Integrity; Dealing with Nonstatic, Unbalanced, and Cost-Sensitive Data; Summary; References; Chapter 2: Machine Learning and Knowledge Discovery; Knowledge Discovery; Classification; Clustering; Dimensionality Reduction; Collaborative Filtering; Machine Learning: Classification Algorithms; Logistic Regression; Random Forest; Hidden Markov Model; Multilayer Perceptron; Machine Learning: Clustering Algorithms; k -Means Clustering; Fuzzy k -Means (Fuzzy c - Means)…”

Tabla de Contenidos: “…The research on dim moving target detection algorithm in deep spaceAn adaptive multi-scale lattice vector quantization and its application in low bit rate speech coding; Sparse coding based kinship recognition; Sparse representation and random forests based face recognition with single sample per person; Practice and thinking of the bilingual teaching for undergraduate digital signal processing course; Interactive random music using breath control for entertainment application; A high efficient and feature lossless image compression approach for image retrieval…”

Tabla de Contenidos: “…Brazil's energy mix; Figure 18. Forest depletion has slowed…”