Index
A
- abstraction
- activation function
- ActivationFunction interface
- Adaline
- adaptive neural networks
- adaptive resonance theory (ART)
- Akaike Information Criteria (AIC) / How many clusters?
- architecture, neural networks
- artificial neural networks (ANN) / How learning helps solving problems
- artificial neural networks (ANNs)
- artificial neuron
B
- backpropagation algorithm
- Bayesian Information Criteria (BIC) / How many clusters?
- bias
- binary classes
- Brazilian Institute of Meteorology (INMET)
C
- categorical data
- Chart class / Visualizing the SOMs
- classification
- classification problems
- clustering tasks
- comma-separated values (CSV) / Loading/selecting data
- competitive layer
- competitive learning
- confusion matrix
- convolutional neural network (CNN)
- cost function
- cross-validation
- customer profiling
D
E
- empirical design, neural networks
- encapsulation
- enrolment status prediction
- external validation / External validation
- extreme learning machines (ELMs)
F
- feedback networks
- feedforward networks
G
H
- Hebbian learning
- hidden layers
- Hidden Markov Model (HMM)
- hybrid neural network
- hybrid systems
I
- inheritance
- input selection
J
K
- Kohonen neural network
- Kohonen self-organizing maps (SOMs)
L
- layers
- learning ability
- learning algorithms
- learning paradigms
- learning process
- learning rate
- Levenberg-Marquardt algorithm
- linear separation
- logistic regression
- long short time memory (LSTM)
M
N
- NaNs / Dropping NaNs
- neighborhood function
- NeuralLayer class
- neural network class
- neural networks
- neural networks, classes
- neuro-fuzzy
- neuro-genetic
- neuron class
- normalization
O
- objects-oriented programming (OOP)
- one-dimensional SOM
- online retraining
- overfitting
- overtraining
P
- pattern recognition
- perceptrons
- polymorphism
- Principal Component Analysis (PCA)
- proben1 dataset
- profiling
- pseudo-algorithm
R
- Radial Basis Function (RBF) / Hybrid systems
- recurrent MLP
- regression
- regression problems
- restricted Boltzmann machine (RBM)
S
- Self-Organizing Maps (SOM) / Hybrid systems
- SOM learning algorithm
- stochastic online learning
- structure selection
- supervised learning
T
- testing
- text recognition
- training
- two-dimensional SOM
U
- undefined classes
- unsupervised learning
- unsupervised learning algorithms
W
- weather database
- weather forecasting
- data, loading / Loading/selecting data, Loading the data and beginning to play!
- data, selecting / Loading/selecting data
- output variable, selecting / Choosing input and output variables
- input variable, selecting / Choosing input and output variables
- preprocessing / Preprocessing
- normalization, implementing / Normalization
- normalization, handling with NeuralDataSet / Adapting NeuralDataSet to handle normalization
- learning algorithm, adapting for normalization / Adapting the learning algorithm to normalization
- Java implementation / Java implementation of weather forecasting
- weather data, collecting / Collecting weather data
- variables, delaying / Delaying variables
- executing / Loading the data and beginning to play!
- correlation analysis, performing / Let's perform a correlation analysis
- neural networks, creating / Creating neural networks
- training / Training and test
- testing / Training and test
- neural network, training / Training the neural network
- error, plotting / Plotting the error
- neural network output, viewing / Viewing the neural network output
- weather forecasting, data selection
- winner-takes-all rule / Competitive learning
X
Z
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