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Hierarchical algorithms find successive clusters using previously established clusters, whereas partitional algorithms determine all clusters at once. Hierarchical algorithms can be agglomerative (bottom-up) or divisive (top-down). Agglomerative algorithms begin with each element as a separate cluster and merge them in successively larger clusters.
In biology supervised learning can be helpful when we have data that we know how to categorize and we would like to categorize more data into those categories. Diagram showing a simple random forest. A common supervised learning algorithm is the random forest, which uses numerous decision trees to train a model to classify a dataset. Forming ...
In the genomic branch of bioinformatics, homology is used to predict the function of a gene: if the sequence of gene A, whose function is known, is homologous to the sequence of gene B, whose function is unknown, one could infer that B may share A's function. In structural bioinformatics, homology is used to determine which parts of a protein ...
The LOCAL algorithm is an improvement of the GLOBAL algorithm presented in Mau, Newton and Larget (1999) [14] in which all branch lengths are changed in every cycle. The LOCAL algorithms modifies the tree by selecting an internal branch of the tree at random. The nodes at the ends of this branch are each connected to two other branches.
A fixed mapping between an initial state and a final state. Starting from an initial condition and moving forward in time, a deterministic process always generates the same trajectory, and no two trajectories cross in state space. Difference equations/Maps – discrete time, continuous state space.
A widely used type of composition is the nonlinear weighted sum, where () = (()), where (commonly referred to as the activation function [3]) is some predefined function, such as the hyperbolic tangent, sigmoid function, softmax function, or rectifier function. The important characteristic of the activation function is that it provides a smooth ...
These show the growth rate of the proportion of organisms using a certain strategy and that rate is equal to the difference between the average payoff of that strategy and the average payoff of the population as a whole. [13] Continuous replicator equations assume infinite populations, continuous time, complete mixing and that strategies breed ...
where as before w ij is the synaptic weight between the i th input and j th output neurons, x is the input, y is the postsynaptic output, and we define ε to be a constant analogous the learning rate, and c pre and c post are presynaptic and postsynaptic functions that model the weakening of signals over time.