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- 2012277057 contributor B12486149.
- 2012277057 contributor B12486150.
- 2012277057 created "2012.".
- 2012277057 date "2012".
- 2012277057 date "2012.".
- 2012277057 dateCopyrighted "2012.".
- 2012277057 description "Contents note continued: Priority Features of Email -- Writing a Priority Inbox -- Functions for Extracting the Feature Set -- Creating a Weighting Scheme for Ranking -- Weighting from Email Thread Activity -- Training and Testing the Ranker -- 5. Regression: Predicting Page Views -- Introducing Regression -- The Baseline Model -- Regression Using Dummy Variables -- Linear Regression in a Nutshell -- Predicting Web Traffic -- Defining Correlation -- 6. Regularization: Text Regression -- Nonlinear Relationships Between Columns: Beyond Straight Lines -- Introducing Polynomial Regression -- Methods for Preventing Overfitting -- Preventing Overfitting with Regularization -- Text Regression -- Logistic Regression to the Rescue -- 7. Optimization: Breaking Codes -- Introduction to Optimization -- Ridge Regression -- Code Breaking as Optimization -- 8. PCA: Building a Market Index -- Unsupervised Learning -- 9. MDS: Visually Exploring US Senator Similarity.".
- 2012277057 description "Includes bibliographical references (p. 293-294) and index.".
- 2012277057 description "Machine generated contents note: 1. Using R -- R for Machine Learning -- Downloading and Installing R -- IDEs and Text Editors -- Loading and Installing R Packages -- R Basics for Machine Learning -- Further Reading on R -- 2. Data Exploration -- Exploration versus Confirmation -- What Is Data? -- Inferring the Types of Columns in Your Data -- Inferring Meaning -- Numeric Summaries -- Means, Medians, and Modes -- Quantiles -- Standard Deviations and Variances -- Exploratory Data Visualization -- Visualizing the Relationships Between Columns -- 3. Classification: Spam Filtering -- This or That: Binary Classification -- Moving Gently into Conditional Probability -- Writing Our First Bayesian Spam Classifier -- Defining the Classifier and Testing It with Hard Ham -- Testing the Classifier Against All Email Types -- Improving the Results -- 4. Ranking: Priority Inbox -- How Do You Sort Something When You Don't Know the Order? -- Ordering Email Messages by Priority.".
- 2012277057 extent "xiii, 303 p. :".
- 2012277057 identifier "1449303714".
- 2012277057 identifier "9781449303716".
- 2012277057 identifier 2012277057-b.html.
- 2012277057 identifier 2012277057-d.html.
- 2012277057 identifier 2012277057-t.html.
- 2012277057 issued "2012".
- 2012277057 issued "2012.".
- 2012277057 language "eng".
- 2012277057 publisher "Sebastopol, CA : O'Reilly Media,".
- 2012277057 subject "005.1 23".
- 2012277057 subject "Computer algorithms.".
- 2012277057 subject "Electronic data processing Automation.".
- 2012277057 subject "QA76.9.A43 C674 2012".
- 2012277057 tableOfContents "Contents note continued: Priority Features of Email -- Writing a Priority Inbox -- Functions for Extracting the Feature Set -- Creating a Weighting Scheme for Ranking -- Weighting from Email Thread Activity -- Training and Testing the Ranker -- 5. Regression: Predicting Page Views -- Introducing Regression -- The Baseline Model -- Regression Using Dummy Variables -- Linear Regression in a Nutshell -- Predicting Web Traffic -- Defining Correlation -- 6. Regularization: Text Regression -- Nonlinear Relationships Between Columns: Beyond Straight Lines -- Introducing Polynomial Regression -- Methods for Preventing Overfitting -- Preventing Overfitting with Regularization -- Text Regression -- Logistic Regression to the Rescue -- 7. Optimization: Breaking Codes -- Introduction to Optimization -- Ridge Regression -- Code Breaking as Optimization -- 8. PCA: Building a Market Index -- Unsupervised Learning -- 9. MDS: Visually Exploring US Senator Similarity.".
- 2012277057 tableOfContents "Machine generated contents note: 1. Using R -- R for Machine Learning -- Downloading and Installing R -- IDEs and Text Editors -- Loading and Installing R Packages -- R Basics for Machine Learning -- Further Reading on R -- 2. Data Exploration -- Exploration versus Confirmation -- What Is Data? -- Inferring the Types of Columns in Your Data -- Inferring Meaning -- Numeric Summaries -- Means, Medians, and Modes -- Quantiles -- Standard Deviations and Variances -- Exploratory Data Visualization -- Visualizing the Relationships Between Columns -- 3. Classification: Spam Filtering -- This or That: Binary Classification -- Moving Gently into Conditional Probability -- Writing Our First Bayesian Spam Classifier -- Defining the Classifier and Testing It with Hard Ham -- Testing the Classifier Against All Email Types -- Improving the Results -- 4. Ranking: Priority Inbox -- How Do You Sort Something When You Don't Know the Order? -- Ordering Email Messages by Priority.".
- 2012277057 title "Machine learning for hackers / Drew Conway and John Myles White.".
- 2012277057 type "text".