| Package | Description |
|---|---|
| smile.association |
Frequent item set mining and association rule mining.
|
| smile.base.cart |
Classification and regression tree base package.
|
| smile.base.mlp |
Multilayer perceptron neural network base package.
|
| smile.base.rbf |
RBF network base package.
|
| smile.base.svm |
Support vector machine base package.
|
| smile.classification |
Classification algorithms.
|
| smile.clustering |
Clustering analysis.
|
| smile.clustering.linkage |
Cluster dissimilarity measures.
|
| smile.feature |
Feature generation, normalization and selection.
|
| smile.glm |
Generalized linear models.
|
| smile.glm.model |
The error distribution models.
|
| smile.imputation |
Missing value imputation.
|
| smile.manifold |
Manifold learning finds a low-dimensional basis for describing
high-dimensional data.
|
| smile.mds |
Multidimensional scaling.
|
| smile.neighbor |
Nearest neighbor search.
|
| smile.neighbor.lsh |
LSH internal classes.
|
| smile.projection |
Feature extraction.
|
| smile.projection.ica |
The contrast functions in FastICA.
|
| smile.regression |
Regression analysis.
|
| smile.sequence |
Learning algorithms for sequence data.
|
| smile.timeseries |
Time series analysis.
|
| smile.validation |
Model validation and selection.
|
| smile.validation.metric | |
| smile.vq |
Vector quantization is a lossy compression technique used in speech
and image coding.
|
| smile.vq.hebb |
Hebbian theory is a neuroscientific theory claiming that an increase in
synaptic efficacy arises from a presynaptic cell's repeated and persistent
stimulation of a postsynaptic cell.
|