models
72
2.4.4.1 The multi-statistics multivariate model
73
2.4.4.2 The multi-replicate multivariate model
74
ary
75
se Cleavage Pattern Discovery
77
biology question — protease cleavage
78
e linear discriminant analysis algorithm
79
.1 The definition and working principle of LDA
80
.2 The projection direction optimisation
81
.3 The formulation of LDA
84
.4 Making decision using the Bayes rule for a LDA model
87
.5 The R function for LDA
89
e other analytic discriminant analysis algorithms
93
.1 The quadratic discriminant analysis algorithm
93
.2 The Naïve Bayes algorithm
95
.3 The logistic regression algorithm
95
.4 The Bayesian linear discriminant analysis
97
aluation and generalisation of a supervised machine learning
odel
98
.1 Confusion matrix
98
.2 Receiver operating characteristic analysis
101
.3 Generalisation
106
ample
109
nlinear algorithms
114
.1 Multi-layer perceptron
115
3.6.1.1 The structure of MLP
115
3.6.1.2 The learning mechanism of MLP
117
3.6.1.3 From SLP (LDA) to MLP
119
3.6.1.4 The R packages for MLP
120
.2 Radial basis function neural network
122
.3 The bio-basis function neural network algorithm
124
3.6.3.1 The bio-basis function neural network algorithm
126
3.6.3.2 The Bayesian BBFNN algorithm
128
3.6.3.3 The orthogonal kernel machine
131
.4 The support vector machine algorithm
132
.5 The relevance vector machine algorithm
137
.6 Deep neural network
139
.7 Inductive learning
141