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Chapter 2
by Akhil Wali
Clojure for Machine Learning
Clojure for Machine Learning
Table of Contents
Clojure for Machine Learning
Credits
About the Author
About the Reviewers
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Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Downloading the example code
Downloading the color images of this book
Errata
Piracy
Questions
1. Working with Matrices
Introducing Leiningen
Representing matrices
Generating matrices
Adding matrices
Multiplying matrices
Transposing and inverting matrices
Interpolating using matrices
Summary
2. Understanding Linear Regression
Understanding single-variable linear regression
Understanding gradient descent
Understanding multivariable linear regression
Gradient descent with multiple variables
Understanding Ordinary Least Squares
Using linear regression for prediction
Understanding regularization
Summary
Understanding the Bayesian classification
Using the k-nearest neighbors algorithm
Using decision trees
Summary
4. Building Neural Networks
Understanding nonlinear regression
Representing neural networks
Understanding multilayer perceptron ANNs
Understanding the backpropagation algorithm
Understanding recurrent neural networks
Building SOMs
Summary
5. Selecting and Evaluating Data
Understanding underfitting and overfitting
Evaluating a model
Understanding feature selection
Varying the regularization parameter
Understanding learning curves
Improving a model
Using cross-validation
Building a spam classifier
Summary
6. Building Support Vector Machines
Understanding large margin classification
Alternative forms of SVMs
Linear classification using SVMs
Using kernel SVMs
Sequential minimal optimization
Using kernel functions
Summary
7. Clustering Data
Using K-means clustering
Clustering data using clj-ml
Using hierarchical clustering
Using Expectation-Maximization
Using SOMs
Reducing dimensions in the data
Summary
8. Anomaly Detection and Recommendation
Detecting anomalies
Building recommendation systems
Content-based filtering
Collaborative filtering
Using the Slope One algorithm
Summary
9. Large-scale Machine Learning
Using MapReduce
Querying and storing datasets
Machine learning in the cloud
Summary
A. References
Chapter 1
Chapter 2
Chapter 3
Chapter 4
Chapter 5
Chapter 6
Chapter 7
Chapter 8
Chapter 9
Index
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Prev
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A. References
Next
Next Chapter
Chapter 3
Chapter 2
Anderson, Edgar. "The Species Problem in Iris". Annals of the Missouri Botanical Garden 23 (3): 457–509. 1936.
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