| Package | Description |
|---|---|
| smile.association |
Frequent item set mining and association rule mining.
|
| smile.base.cart | |
| smile.base.mlp | |
| smile.base.rbf | |
| smile.base.svm | |
| smile.classification |
Classification algorithms.
|
| smile.clustering |
Clustering analysis.
|
| smile.clustering.linkage |
Cluster dissimilarity measures.
|
| smile.feature |
Feature generation, normalization and selection.
|
| smile.gap |
Genetic algorithm and programming.
|
| 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 | |
| smile.projection |
Feature extraction.
|
| smile.projection.ica | |
| smile.regression |
Regression analysis.
|
| smile.sampling |
Sampling is concerned with the selection of a subset of individuals
from within a statistical population to estimate characteristics of
the whole population.
|
| smile.sequence |
Learning algorithms for sequence data.
|
| smile.taxonomy |
A taxonomy is a tree of terms (concepts) where leaves
must be named but intermediary nodes can be anonymous.
|
| smile.validation |
Model validation.
|
| 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.
|
| smile.wavelet |
Discrete wavelet transform (DWT).
|