Presents both theory and techniques behind the concepts and practice of pattern recognition. CD-ROM contains a fully functional trial version of Pattern Recognition Workbench -- a powerful, easy-to-use system with the latest machine learning, neural network, and statistical algorithms. It provides a complete working environment to solve your pattern recognition problems.
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Apply pattern recognition to find the hidden gems in your data!
Data mining technology is helping businesses everywhere to work smarter by revealing unknown patterns within existing archives. Applying the latest advances in pattern recognition software can give you a key competitive edge across all data mining applications. The tutorials and software package included in Solving Data Mining Problems through Pattern Recognition take advantage of machine learning techniques and neural networks to help you get the most out of your data. Besides explaining the most current theories, Solving Data Mining Problems through Pattern Recognition takes a practical approach to overall project development concerns.
The rigorous, multi-step method includes:
Pattern classification, estimation, and modeling are addressed using the following algorithms:
While some aspects of pattern recognition involve advanced mathematical principles, most successful projects rely on a strong element of human experience and intuition. Solving Data Mining Problems through Pattern Recognition provides a strong theoretical grounding for beginners, yet it also contains detailed models and insights into real-world problem-solving that will inspire more experienced users, be they database designers, modelers, or project leaders.
This book includes a free, 90-day trial copy of Pattern Recognition Workbench, a powerful, easy-to-use system that combines machine learning, neural networks, and statistical algorithms to help you apply pattern recognition to your data right now. The enclosed CD-ROM runs under Windows(r) 95 and Windows NT(tm).
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Book Description Prentice Hall, 1997. Hardcover. Book Condition: New. New Book. Lightest of shelf/storage wear. Sealed CD Included! SHIPS WITHIN 24 HOURS! Tracking Provided. DHL processing & USPS delivery for an average of 3-5 Day Standard & 2-3 Day Expedited! FREE INSURANCE! Fast & Personal Support! Careful Packaging. No Hassle, Full Refund Return Policy!. Bookseller Inventory # mon0000189109
Book Description Prentice Hall, 1997. Book Condition: New. Brand New, Unread Copy in Perfect Condition. A+ Customer Service! Summary: 1. Introduction . Pattern Recognition by Humans. Pattern Recognition by Computers. Data Mining and Pattern Recognition. Types of Pattern Recognition. Classification. Calculation in Classification. Uncertainty in Classification. Computer-Automated Classification.Estimation. Calculation in Estimation. Uncertainty in Estimation. Computer-Automated Estimation. Developing a Model. Fixed Models. Parametric Models. Nonparametric Models. Preprocessing. A Continuum of Methods. Biases Due to Prior Knowledge. The Purpose of this Book. 2. Key Concepts: Estimation. Terminology and Notation. Characteristics of an Optimal Model. Sources of Error. Fixed Models. Parametric Models. Example: Linear Regression. Generalization. Shortcomings of Parametric Methods. Iteration through Parametric Forms. Nonparametric Models. The Underlying Modeling Problem. Heuristics in Nonparametric Modeling. Approximation Architectures. A Practical Nonparametric Approach. The Role of Preprocessing. Statistical Considerations. 3. Key Concepts: Classification. Terminology and Notation. Characteristics of an Optimal Classifier. Types of Models. Decision-Region Boundaries. Probability Density Functions. Posterior Probabilities. Approaches to Modeling. Fixed Models. Parametric Models. Nonparametric Models. The Role of Preprocessing. The Importance of Multiple Techniques. Appendix. Statistical Considerations. 4. Additional Application Areas. Database Marketing. Response Modeling. Cross Selling. Time-Series Prediction. Detection. Probability Estimation. Information Compression. Sensitivity Analysis. 5. Overview of the Development Process. Defining the Pattern Recognition Problem. Collecting Data. Preparing Data. Preprocessing. Selecting an Algorithm and Training Parameters. Training and Testing. Iterating Steps and Troubleshooting. Appendix. Evaluating the Final Model. 6. Defining the Pattern Recognition Problem. What Problems Are Suitable for Data-Driven Solutions? How Do You Evaluate Results? Is It a Classification or Estimation Problem?What Are the Inputs and Outputs? Appendix. Defining the Problem in PRW. 7. Collecting Data. What Data to Collect. How to Collect Data. How Much Data Is Enough. Using Simulated Data. Appendix. Importing Data into PRW. 8. Preparing Data. Transforming Data into Numerical Values. Inconsistent Data and Outliers. Appendix. Preparing Data in PRW. Handling Missing Data. Converting Non-Numeric Inputs. Handling Inconsistent Data or Outliers. 9. Data Preprocessing. Why Should You Preprocess Your Data? Averaging Data Values. Thresholding Data. Reducing the Input Space. Normalizing Data. Why Normalize Data? Types of Normalization. Modifying Prior Probabilities. Other Considerations. Appendix A: Preprocessing in PRW. Averaging Time-Series Data. Thresholding and Replacing Input Values. Reducing the Input Space. Normalizing Data. Modifying Prior Input Probabilities. 10. Selecting Architectures and Training Parameters. Types of Algorithms. How to Pick an Algorithm.Practical Constraints. Memory Usage. Training Times. Classification/Estimation Times. Algorithm Descriptions. Linear Regression. Logistic Regression. Unimodal Gaussian. Multilayered Perceptron/Backpropagation. Radial Basis Functions. K Nearest Neighbors. Gaussian Mixture. Nearest Cluster. K Means Clustering. Decision Trees. Other Nonparametric Architectures. Algorithm Comparison Summary. Appendix A: Selecting Algorithms and Training Parameters in PRW. Selecting an Algorithm in PRW. Setting Algorithm Parameters. Linear. Bookseller Inventory # ABE_book_new_0130950831
Book Description Prentice Hall, 1997. Hardcover. Book Condition: New. book. Bookseller Inventory # 0130950831
Book Description Hardcover. Book Condition: BRAND NEW. BRAND NEW. Fast Shipping. Prompt Customer Service. Satisfaction guaranteed. Bookseller Inventory # 0130950831BNA
Book Description Book Condition: Brand New. Book Condition: Brand New. Bookseller Inventory # 97801309508331.0