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Chapter 9
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
www.PacktPub.com
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Free Access for Packt account holders
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
Previous Chapter
Chapter 9
Index
A
accumulator function /
Detecting anomalies
activation function, neuron
about /
Representing neural networks
activation of neuron
about /
Representing neural networks
Adaptive Resonance Theory (ART) /
Understanding the backpropagation algorithm
add-lines function /
Understanding single-variable linear regression
add-points function /
Interpolating using matrices
agglomerative clustering
about /
Using hierarchical clustering
complete linkage clustering /
Using hierarchical clustering
single linkage clustering /
Using hierarchical clustering
analyze-results function /
Building a spam classifier
anomalies
detecting /
Detecting anomalies
detecting, proximity-based approach used /
Detecting anomalies
detecting, density-based approach used /
Detecting anomalies
detecting, clustering-based approaches used /
Detecting anomalies
anomaly detection
about /
Detecting anomalies
using /
Detecting anomalies
probability distribution model, using /
Detecting anomalies
statistical methods /
Detecting anomalies
proximity-based approach, using /
Detecting anomalies
density-based approach, using /
Detecting anomalies
clustering-based approaches, using /
Detecting anomalies
anomaly detector, Clojure
implementation, demonstrating /
Detecting anomalies
Apache SpamAssassin project /
Building a spam classifier
api/get-final function /
Machine learning in the cloud
apply-weight-changes function /
Understanding the backpropagation algorithm
atom-hash-map function /
Detecting anomalies
await function /
Building a spam classifier
B
back-propagate-layer function /
Understanding the backpropagation algorithm
backpropagation learning algorithm
used, for training multilayer perceptron ANNs /
Understanding the backpropagation algorithm
summarizing /
Understanding the backpropagation algorithm
band matrix /
Interpolating using matrices
Basic Linear Algebra Subprograms (BLAS) specification /
Representing matrices
bayes-classifier variable /
Understanding the Bayesian classification
Bayes classifier
about /
Understanding the Bayesian classification
bayesian-spam-probability function /
Building a spam classifier
Bayesian classification
about /
Understanding the Bayesian classification
example /
Understanding the Bayesian classification
Bayesian probability function /
Building a spam classifier
BigML
URL /
Machine learning in the cloud
about /
Machine learning in the cloud
working /
Machine learning in the cloud
authentication details, providing /
Machine learning in the cloud
binary classification
about /
Understanding the binary and multiclass classification
examples /
Understanding the binary and multiclass classification
modeling, sigmoid/logistic function used /
Understanding the binary and multiclass classification
bind-columns function /
Understanding Ordinary Least Squares
bind-rows function /
Understanding the backpropagation algorithm
C
calc-deltas function /
Understanding the backpropagation algorithm
calc-gradients-and-error function /
Understanding the backpropagation algorithm
calc-gradients function /
Understanding the backpropagation algorithm
calculate-probability-of-category function /
Understanding the Bayesian classification
CARTs
about /
Machine learning in the cloud
centroid function /
Using hierarchical clustering
cl/clatrix? function /
Representing matrices
cl/get function /
Representing matrices
cl/map-indexed function
used, for creating identity matrix /
Generating matrices
cl/rnorm function
overloads /
Generating matrices
classification
binary classification /
Understanding the binary and multiclass classification
classifier-classify function /
Using the k-nearest neighbors algorithm
,
Using decision trees
classifier-evaluate function /
Using cross-validation
classifier-train function /
Understanding the Bayesian classification
,
Using decision trees
classify-by-attrs function /
Understanding the Bayesian classification
classify function /
Building a spam classifier
classify method /
Building SOMs
clatrix
URL /
Representing matrices
clatrix library
working /
Representing matrices
matrix, creating from /
Representing matrices
cl/map-indexed function /
Representing matrices
cl/map function /
Representing matrices
clear-db function /
Building a spam classifier
clj-ml library
URL /
Understanding the Bayesian classification
used, for clustering data /
Clustering data using clj-ml
Clojure
matrix, representing /
Representing matrices
closest-vectors function /
Using hierarchical clustering
clusterer-cluster function /
Clustering data using clj-ml
clustering-based approaches
used, for anomaly detection /
Detecting anomalies
Cobweb algorithm
about /
Using hierarchical clustering
coefficient of determination
about /
Understanding single-variable linear regression
cofactor matrix /
Transposing and inverting matrices
collaborative filtering
about /
Building recommendation systems
advantage /
Collaborative filtering
working /
Collaborative filtering
combining, with content-based filtering /
Collaborative filtering
Slope One algorithm /
Using the Slope One algorithm
column-count function /
Representing matrices
compute-matrix function /
Generating matrices
conceptual clustering
about /
Using hierarchical clustering
conditional probability /
Understanding the Bayesian classification
confusion matrix
about /
Evaluating a model
connection weights
about /
Building SOMs
constantly function /
Building a spam classifier
content-based filtering
about /
Building recommendation systems
working /
Content-based filtering
disadvantage /
Content-based filtering
core.matrix library
about /
Representing matrices
URL /
Representing matrices
cost function
about /
Understanding single-variable linear regression
representation /
Understanding single-variable linear regression
Euclidian space /
Understanding single-variable linear regression
plotting /
Understanding single-variable linear regression
covariance matrix
about /
Reducing dimensions in the data
criterium library
URL /
Multiplying matrices
cross-validation
using /
Using cross-validation
types /
Using cross-validation
cross-validation set
about /
Evaluating a model
,
Using cross-validation
cross-validation types
k-fold cross-validation /
Using cross-validation
2-fold cross-validation/holdout method /
Using cross-validation
repeated random subsampling /
Using cross-validation
leave-one-out cross-validation /
Using cross-validation
CSV (comma-separated values) /
Machine learning in the cloud
Cumulative Distribution Function (CDF) /
Building a spam classifier
cv-from-corpus function /
Building a spam classifier
D
data
clustering, clj-ml library used /
Clustering data using clj-ml
dataset-set-class function /
Using cross-validation
datasets
querying /
Querying and storing datasets
storing /
Querying and storing datasets
decision boundary
about /
Understanding the binary and multiclass classification
working /
Understanding the binary and multiclass classification
decision tree learning
about /
Using decision trees
decision trees
using /
Using decision trees
default-options variable /
Understanding the backpropagation algorithm
dendogram
about /
Using hierarchical clustering
density-based approach
used, for anomaly detection /
Detecting anomalies
PDF, using /
Detecting anomalies
density-detector function /
Detecting anomalies
density function /
Detecting anomalies
determinant
defining /
Transposing and inverting matrices
singular matrix /
Transposing and inverting matrices
calculating, Sarrus rule used /
Transposing and inverting matrices
determinant (REPL)
calculating, det function used /
Transposing and inverting matrices
det function
used, for calculating determinant /
Transposing and inverting matrices
dimensionality reduction
about /
Reducing dimensions in the data
PCA /
Reducing dimensions in the data
working /
Reducing dimensions in the data
divisive clustering
about /
Using hierarchical clustering
E
Elman neural network
about /
Understanding recurrent neural networks
context layer /
Understanding recurrent neural networks
structure /
Understanding recurrent neural networks
training /
Understanding recurrent neural networks
EM clusterer
using /
Using Expectation-Maximization
parameter MLE, determining /
Using Expectation-Maximization
creating, make-clusterer function used /
Using Expectation-Maximization
training /
Using Expectation-Maximization
EM clusterer steps
expectation step /
Using Expectation-Maximization
maximization step /
Using Expectation-Maximization
Enclog library
URL /
Understanding the backpropagation algorithm
evaluation/create function /
Machine learning in the cloud
every? function /
Adding matrices
evidence-category-with-attrs function /
Understanding the Bayesian classification
evidence-of-salmon function /
Understanding the Bayesian classification
evidence-of-sea-bass function /
Understanding the Bayesian classification
extend-type function /
Using hierarchical clustering
extract-features function /
Building a spam classifier
extract-tokens-from-headers function /
Building a spam classifier
extract-tokens function /
Building a spam classifier
extract-words function /
Linear classification using SVMs
F
farthest neighbor clustering
about /
Using hierarchical clustering
feature map
connection weights /
Building SOMs
feature selection
about /
Understanding feature selection
final-with-default-connection function /
Machine learning in the cloud
fish-template vector /
Using cross-validation
fisher function /
Building a spam classifier
Fisher method /
Building a spam classifier
flatten-to-vec function /
Using the Slope One algorithm
Forgy method /
Using K-means clustering
forward-propagate-all-activations function /
Understanding the backpropagation algorithm
forward-propagate-layer function /
Understanding the backpropagation algorithm
forward-propagate function /
Understanding the backpropagation algorithm
frequencies function /
Using K-means clustering
G
Gaussian kernel function
about /
Using kernel SVMs
get-dataset function /
Understanding multivariable linear regression
get-or-add-key function /
Detecting anomalies
gradient-descent-bprop function /
Understanding the backpropagation algorithm
gradient-descent-complete? function /
Understanding the backpropagation algorithm
gradient-descent function /
Understanding the backpropagation algorithm
gradient descent algorithm
about /
Understanding gradient descent
formal representation /
Understanding gradient descent
step /
Understanding gradient descent
partial derivative component, simplifying /
Understanding gradient descent
simplified version, implementing /
Understanding gradient descent
using, with multiple variables /
Gradient descent with multiple variables
gradients-and-error function /
Understanding the backpropagation algorithm
grouper function /
Using K-means clustering
H
h-clusterer variable /
Using hierarchical clustering
header-token-regex function /
Building a spam classifier
hidden-activations variable /
Understanding the backpropagation algorithm
hidden-weights variable /
Understanding the backpropagation algorithm
hierarchical clustering
using /
Using hierarchical clustering
linkage criteria /
Using hierarchical clustering
Cobweb algorithm /
Using hierarchical clustering
implementation /
Using hierarchical clustering
high bias
about /
Understanding underfitting and overfitting
high variance
about /
Understanding underfitting and overfitting
homogenous kernel
about /
Using kernel SVMs
I
identity matrix
about /
Generating matrices
creating, used cl/map-indexed function /
Generating matrices
if-let function /
Building a spam classifier
inc-count function /
Building a spam classifier
inc-total-count! function /
Building a spam classifier
Incanter library
URL /
Interpolating using matrices
used, for building SOM /
Using SOMs
used, for querying datasets /
Querying and storing datasets
used, for storing datasets /
Querying and storing datasets
Information entropy
defining /
Using decision trees
insert-dataset function /
Querying and storing datasets
inverse function /
Transposing and inverting matrices
iris-features variable /
Using SOMs
Iris dataset
obtaining, with get-dataset function /
Understanding multivariable linear regression
iterate-means function /
Using K-means clustering
K
k-fold cross-validation variations
2-fold cross-validation /
Using cross-validation
leave-one-out cross-validation /
Using cross-validation
k-means-clusterer variable /
Clustering data using clj-ml
K-means clustering algorithm
about /
Using K-means clustering
using /
Using K-means clustering
iterations, performing /
Using K-means clustering
optimization objective /
Using K-means clustering
K-means clustering algorithm steps
assignment step /
Using K-means clustering
update step /
Using K-means clustering
k-NN algorithm
about /
Using the k-nearest neighbors algorithm
using /
Using the k-nearest neighbors algorithm
nearest neighbor algorithm /
Using the k-nearest neighbors algorithm
kernel functions
about /
Alternative forms of SVMs
polynomial kernel function /
Using kernel SVMs
Gaussian kernel function /
Using kernel SVMs
string kernel function /
Using kernel SVMs
using /
Using kernel functions
kernel SVMs
using /
Using kernel SVMs
optimization problem, solving with SMO /
Sequential minimal optimization
kernel functions, using /
Using kernel functions
known-items function /
Using the Slope One algorithm
L
large margin classification
about /
Understanding large margin classification
SVMs alternative forms /
Alternative forms of SVMs
lateral interaction
about /
Building SOMs
learning curves
about /
Understanding learning curves
used, for visualizing machine performance /
Understanding learning curves
Leiningen
URL /
Introducing Leiningen
about /
Introducing Leiningen
LibLinear
URL /
Linear classification using SVMs
LibSVM /
Sequential minimal optimization
linear-model function /
Understanding single-variable linear regression
Linear Algebra /
Representing matrices
linear discriminant analysis (LDA) method /
Understanding multivariable linear regression
linear interpolation
about /
Using the k-nearest neighbors algorithm
linear regression model
about /
Understanding single-variable linear regression
using, with single variable /
Understanding single-variable linear regression
using, with multiple variables /
Understanding multivariable linear regression
using, for prediction /
Using linear regression for prediction
logistic regression
about /
Understanding the binary and multiclass classification
log likelihood function
expected value, calculating /
Using Expectation-Maximization
M
M/* function
used, for matrix multiplication /
Multiplying matrices
M/+ function
used, for matrix addition /
Adding matrices
machine learning, cloud
BigML /
Machine learning in the cloud
make-category-probability-pair function /
Understanding the Bayesian classification
make-classifier function /
Understanding the Bayesian classification
,
Using decision trees
,
Using cross-validation
make-connection function /
Machine learning in the cloud
make-dataset function /
Understanding the Bayesian classification
make-instance function /
Understanding the Bayesian classification
make-sample-fish function /
Understanding the Bayesian classification
,
Using cross-validation
map-nested-vals function /
Using the Slope One algorithm
map-reduce function /
Using MapReduce
map-vals function /
Using the Slope One algorithm
mapmap function /
Using the Slope One algorithm
MapReduce
using /
Using MapReduce
about /
Using MapReduce
Map() step /
Using MapReduce
Reduce() step /
Using MapReduce
Partition() step /
Using MapReduce
mapv function /
Adding matrices
matrices
representing /
Representing matrices
core.matrix library /
Representing matrices
representing, in Clojure /
Representing matrices
generating /
Generating matrices
adding /
Adding matrices
equality equation /
Adding matrices
multiplying /
Multiplying matrices
transposing /
Transposing and inverting matrices
inverting /
Transposing and inverting matrices
determinant, defining /
Transposing and inverting matrices
used, for interpolating /
Interpolating using matrices
matrix
creating, from vector of vectors /
Representing matrices
creating, from clatrix library /
Representing matrices
square matrix /
Generating matrices
identity matrix /
Generating matrices
matrix-matrix multiplication /
Multiplying matrices
matrix-mult function /
Understanding the backpropagation algorithm
matrix-vector multiplication
about /
Multiplying matrices
matrix? function /
Representing matrices
matrix function /
Representing matrices
Maximum a Posteriori (MAP) estimation /
Understanding the Bayesian classification
MLE
about /
Using Expectation-Maximization
mmult function /
Understanding the backpropagation algorithm
model
underfit model /
Understanding underfitting and overfitting
overfit model /
Understanding underfitting and overfitting
evaluating /
Evaluating a model
test set /
Evaluating a model
test error /
Evaluating a model
training error /
Evaluating a model
features, selecting /
Understanding feature selection
performance, visualizing with learning curves /
Understanding learning curves
improving /
Improving a model
cross-validation set /
Using cross-validation
MSE
formally defining /
Understanding single-variable linear regression
MSE, ANN
calculating /
Understanding the backpropagation algorithm
multiclass classification
about /
Understanding the binary and multiclass classification
one-vs-all classification /
Understanding the binary and multiclass classification
multilayer perceptron ANNs
about /
Understanding multilayer perceptron ANNs
illustration /
Understanding multilayer perceptron ANNs
perceptron nodes /
Understanding multilayer perceptron ANNs
training, backpropagation algorithm used /
Understanding multilayer perceptron ANNs
,
Understanding the backpropagation algorithm
cost function /
Understanding multilayer perceptron ANNs
multivariable linear regression
about /
Understanding multivariable linear regression
example problem /
Understanding multivariable linear regression
Iris dataset /
Understanding multivariable linear regression
parameter vector /
Understanding multivariable linear regression
polynomial functions, reducing /
Understanding multivariable linear regression
cost function /
Understanding multivariable linear regression
N
nearest neighbor algorithm /
Using the k-nearest neighbors algorithm
neural-pattern function /
Understanding the backpropagation algorithm
neural networks
representing /
Representing neural networks
new-gradient-matrix function /
Understanding the backpropagation algorithm
new-means function /
Using K-means clustering
new-token function /
Building a spam classifier
nonlinear regression
understanding /
Understanding nonlinear regression
normal distribution
used, for training anomaly detector /
Detecting anomalies
O
ols-linear-model function /
Understanding Ordinary Least Squares
OLS method
about /
Understanding Ordinary Least Squares
working /
Understanding Ordinary Least Squares
one-vs-all classification
about /
Understanding the binary and multiclass classification
ordinary-least squares (OLS) curve-fitting algorithm /
Understanding single-variable linear regression
outlier
about /
Detecting anomalies
overfit model
about /
Understanding underfitting and overfitting
overfitting
about /
Understanding underfitting and overfitting
P
parameter vector
about /
Understanding multivariable linear regression
partition-all function /
Using MapReduce
PCA
covariance matrix /
Reducing dimensions in the data
SVD /
Reducing dimensions in the data
performing /
Reducing dimensions in the data
PDF
about /
Detecting anomalies
plot-iris-linear-model function /
Understanding multivariable linear regression
plot-means function /
Using SOMs
plot-rand-sample function /
Interpolating using matrices
plot-reduced-features function /
Reducing dimensions in the data
point-groups function /
Using K-means clustering
polynomial kernel function
about /
Using kernel SVMs
populate-emails function /
Building a spam classifier
predict-svm function /
Linear classification using SVMs
predict function /
Linear classification using SVMs
,
Using the Slope One algorithm
prediction
linear regression model, using for /
Using linear regression for prediction
prediction/create function /
Machine learning in the cloud
prediction/predictor function /
Machine learning in the cloud
primal form /
Alternative forms of SVMs
principal-components function /
Reducing dimensions in the data
principle of topographic formation
about /
Building SOMs
probability distribution model
using, for anomaly detection /
Detecting anomalies
statistical methods /
Detecting anomalies
probability function /
Understanding the Bayesian classification
problem function /
Interpolating using matrices
proximity-based approach
used, for anomaly detection /
Detecting anomalies
Q
quantization
about /
Using K-means clustering
performing, reasons /
Using K-means clustering
R
radial bias function /
Building SOMs
rand-in-range function /
Understanding the Bayesian classification
rand-int function /
Generating matrices
rand-list function /
Understanding the backpropagation algorithm
rand-square-mat function /
Generating matrices
random-initial-weights function /
Understanding the backpropagation algorithm
re-pattern function /
Building a spam classifier
recommendation systems
building /
Building recommendation systems
collaborative filtering, using /
Building recommendation systems
content-based filtering, using /
Building recommendation systems
hybrid methods /
Collaborative filtering
recurrent neural networks
about /
Understanding recurrent neural networks
Elman neural network /
Understanding recurrent neural networks
reduced rotation matrix
about /
Reducing dimensions in the data
reduce function /
Adding matrices
reduction component
about /
Reducing dimensions in the data
references
about /
Chapter 1
,
Chapter 6
,
Chapter 8
regularization
about /
Understanding regularization
Tikhnov regularization /
Understanding regularization
regularization matrix
about /
Understanding regularization
regularization parameter
varying /
Varying the regularization parameter
repeatedly function /
Generating matrices
,
Linear classification using SVMs
repeat function /
Generating matrices
REPL
about /
Introducing Leiningen
rest function /
Understanding the backpropagation algorithm
result-type function /
Building a spam classifier
RMSE
about /
Understanding single-variable linear regression
formally defining /
Understanding single-variable linear regression
rotation component
about /
Reducing dimensions in the data
row-count function /
Representing matrices
run-network function /
Understanding the backpropagation algorithm
S
Sarrus rule
used, for calculating determinant /
Transposing and inverting matrices
scatter-plot function /
Understanding single-variable linear regression
sigmoid function
about /
Understanding the binary and multiclass classification
simplified version, gradient descent algorithm
implementing /
Understanding gradient descent
,
Understanding multivariable linear regression
single-variable linear regression
about /
Understanding single-variable linear regression
example problem /
Understanding single-variable linear regression
example problem, scatter plot /
Understanding single-variable linear regression
SSE /
Understanding single-variable linear regression
MSE /
Understanding single-variable linear regression
RMSE /
Understanding single-variable linear regression
cost function /
Understanding single-variable linear regression
singular value
about /
Reducing dimensions in the data
Slope One algorithm
using /
Using the Slope One algorithm
SMO
using, to solve optimization problem in SVMs /
Sequential minimal optimization
smoothing matrix
about /
Understanding regularization
SOM
building /
Building SOMs
feature map /
Building SOMs
self-organizing process /
Building SOMs
training /
Building SOMs
used, for clustering input data /
Using SOMs
som-batch-train function /
Using SOMs
som variable /
Building SOMs
spam-probability function /
Building a spam classifier
spam classification
about /
Building a spam classifier
spam classifier
building /
Building a spam classifier
training /
Building a spam classifier
features /
Building a spam classifier
cross-validating /
Building a spam classifier
overall error, improving /
Building a spam classifier
spam score /
Building a spam classifier
SPECT Heart dataset
URL /
Sequential minimal optimization
SPECT images
about /
Sequential minimal optimization
square-mat function /
Generating matrices
square matrix
about /
Generating matrices
SSE
about /
Understanding single-variable linear regression
formally defining /
Understanding single-variable linear regression
statistical methods
about /
Detecting anomalies
step, gradient descent algorithm
about /
Understanding gradient descent
string kernel function
about /
Using kernel SVMs
sum-of-evidences parameter /
Understanding the Bayesian classification
sum-of-squares function /
Using hierarchical clustering
SVD
covariance matrix, determining /
Reducing dimensions in the data
about /
Reducing dimensions in the data
SVMs
alternative forms /
Alternative forms of SVMs
used, for performing linear classification /
Linear classification using SVMs
symmetry breaking
about /
Understanding the backpropagation algorithm
synapse
about /
Representing neural networks
T
take-while-unstable function /
Using K-means clustering
test-classifier! function /
Building a spam classifier
Tichonov regularization /
Interpolating using matrices
Tikhnov regularization
about /
Understanding regularization
describing /
Understanding regularization
train-and-cv-classifier function /
Building a spam classifier
train-and-run-som function /
Building SOMs
train-ann function /
Understanding the backpropagation algorithm
train-bayes-classifier function /
Understanding the Bayesian classification
train-clusterer function /
Using Expectation-Maximization
train-DT-classifier function /
Using decision trees
train-from-corpus! function /
Building a spam classifier
train-K1-classifier function /
Using the k-nearest neighbors algorithm
train-network function /
Understanding the backpropagation algorithm
,
Understanding recurrent neural networks
train-som function /
Building SOMs
train-svm function /
Linear classification using SVMs
train-UDT-classifier function /
Using decision trees
trainer function /
Understanding the backpropagation algorithm
,
Building SOMs
train function /
Understanding the backpropagation algorithm
,
Linear classification using SVMs
transpose of matrix
about /
Transposing and inverting matrices
obtaining, ways /
Transposing and inverting matrices
true? function /
Adding matrices
U
underfit model
about /
Understanding underfitting and overfitting
underfitting
about /
Understanding underfitting and overfitting
update-feature! function /
Building a spam classifier
update-features! function /
Building a spam classifier
update-fn function /
Using the Slope One algorithm
update-in function /
Using the Slope One algorithm
V
vec-distance function /
Using K-means clustering
vector of vectors
matrix, creating from /
Representing matrices
vectorz-clj
URL /
Representing matrices
W
Weka library
URL /
Understanding the Bayesian classification
winning neuron
about /
Building SOMs
with-connection function /
Machine learning in the cloud
with-data function /
Querying and storing datasets
X
xy-plot function /
Interpolating using matrices
,
Understanding multivariable linear regression
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