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... function h : X → {0, 1} we define the error of h with respect to P as ErrP (h) = Pr(x,y)∼P (y 6= h(x)). For a class H of hypotheses on X, let the smallest error of a hypothesis h ∈ H with respect to P be denoted by optH (P ) := minh∈H ErrP (h). Given a hypothesis class H, an H-proper SSL-learner tak ...
... function h : X → {0, 1} we define the error of h with respect to P as ErrP (h) = Pr(x,y)∼P (y 6= h(x)). For a class H of hypotheses on X, let the smallest error of a hypothesis h ∈ H with respect to P be denoted by optH (P ) := minh∈H ErrP (h). Given a hypothesis class H, an H-proper SSL-learner tak ...
a hybrid classification model employing genetic
... individuals, where each individual is a candidate solution to a given problem. Initially, a set of random individuals (an individual represents a set of attributes in this case) is selected. The fitness of these individuals is computed through its ability to generate the best RGDT. Hence fitness is ...
... individuals, where each individual is a candidate solution to a given problem. Initially, a set of random individuals (an individual represents a set of attributes in this case) is selected. The fitness of these individuals is computed through its ability to generate the best RGDT. Hence fitness is ...
Affine Independent Variational Inference
... parameters θ can control higher order moments of the approximating density q(w) such as skewness and kurtosis. We can therefore jointly optimise over all parameters {A, b, θ} simultaneously; this means that we can fully capitalise on the expressiveness of the AI distribution class, allowing us to ca ...
... parameters θ can control higher order moments of the approximating density q(w) such as skewness and kurtosis. We can therefore jointly optimise over all parameters {A, b, θ} simultaneously; this means that we can fully capitalise on the expressiveness of the AI distribution class, allowing us to ca ...
Reinforcement Learning Algorithms for MDPs
... Algorithm 1 The function implementing the TD(λ) algorithm with linear function approximation. This function must be called after each transition. function TDLambdaLinFApp(X, R, Y, θ, z) Input: X is the last state, Y is the next state, R is the immediate reward associated with this transition, θ ∈ R ...
... Algorithm 1 The function implementing the TD(λ) algorithm with linear function approximation. This function must be called after each transition. function TDLambdaLinFApp(X, R, Y, θ, z) Input: X is the last state, Y is the next state, R is the immediate reward associated with this transition, θ ∈ R ...
unsupervised static discretization methods
... There is a large variety of discretization methods. Dougherty et al. (1995) [3] present a systematic survey of all the discretization method developed by that time. They also make a first classification of discretization methods based on three directions: global vs. local, supervised vs. unsupervise ...
... There is a large variety of discretization methods. Dougherty et al. (1995) [3] present a systematic survey of all the discretization method developed by that time. They also make a first classification of discretization methods based on three directions: global vs. local, supervised vs. unsupervise ...
Expectation–maximization algorithm
In statistics, an expectation–maximization (EM) algorithm is an iterative method for finding maximum likelihood or maximum a posteriori (MAP) estimates of parameters in statistical models, where the model depends on unobserved latent variables. The EM iteration alternates between performing an expectation (E) step, which creates a function for the expectation of the log-likelihood evaluated using the current estimate for the parameters, and a maximization (M) step, which computes parameters maximizing the expected log-likelihood found on the E step. These parameter-estimates are then used to determine the distribution of the latent variables in the next E step.