Exact discovery of length-range motifs
... The algorithm starts by selecting a random set of Nr reference points. The algorithm works in two phases: The first phase (called hereafter referencing phase) is used to calculate both the upper limit on best motif distance and a lower limit on distances of all possible pairs. During this phase, dis ...
... The algorithm starts by selecting a random set of Nr reference points. The algorithm works in two phases: The first phase (called hereafter referencing phase) is used to calculate both the upper limit on best motif distance and a lower limit on distances of all possible pairs. During this phase, dis ...
An Adaptive Restarting Genetic Algorithm for Global
... Therefore, to make a fair comparison, the maximum number of objective function evaluations of the proposed GA was set exactly the same as in the publication of Nasir and Tokhi [29] as indicated in Table 3. In addition, a traditional GA, which is exactly the same as the proposed GA except two things: ...
... Therefore, to make a fair comparison, the maximum number of objective function evaluations of the proposed GA was set exactly the same as in the publication of Nasir and Tokhi [29] as indicated in Table 3. In addition, a traditional GA, which is exactly the same as the proposed GA except two things: ...
Discrete Structures - CSIS121
... The time required by Dijkstra's algorithm is O(|V|2). It will be reduced to O(|E|log|V|) if heap is used to keep {vV\Si : L(v) < }, where Si is the set S after iteration i. ...
... The time required by Dijkstra's algorithm is O(|V|2). It will be reduced to O(|E|log|V|) if heap is used to keep {vV\Si : L(v) < }, where Si is the set S after iteration i. ...
第頁共9頁 Machine Learning Final Exam. Student No.: Name: 104/6
... 1. (5%) Using principal components analysis, we can find a low-dimensional space such that when x is projected there, information loss is minimized. Let the projection of x on the direction of w is z = wTx. The PCA will find w such that Var(z) is maximized ...
... 1. (5%) Using principal components analysis, we can find a low-dimensional space such that when x is projected there, information loss is minimized. Let the projection of x on the direction of w is z = wTx. The PCA will find w such that Var(z) is maximized ...
Algorithm
In mathematics and computer science, an algorithm (/ˈælɡərɪðəm/ AL-gə-ri-dhəm) is a self-contained step-by-step set of operations to be performed. Algorithms exist that perform calculation, data processing, and automated reasoning.An algorithm is an effective method that can be expressed within a finite amount of space and time and in a well-defined formal language for calculating a function. Starting from an initial state and initial input (perhaps empty), the instructions describe a computation that, when executed, proceeds through a finite number of well-defined successive states, eventually producing ""output"" and terminating at a final ending state. The transition from one state to the next is not necessarily deterministic; some algorithms, known as randomized algorithms, incorporate random input.The concept of algorithm has existed for centuries, however a partial formalization of what would become the modern algorithm began with attempts to solve the Entscheidungsproblem (the ""decision problem"") posed by David Hilbert in 1928. Subsequent formalizations were framed as attempts to define ""effective calculability"" or ""effective method""; those formalizations included the Gödel–Herbrand–Kleene recursive functions of 1930, 1934 and 1935, Alonzo Church's lambda calculus of 1936, Emil Post's ""Formulation 1"" of 1936, and Alan Turing's Turing machines of 1936–7 and 1939. Giving a formal definition of algorithms, corresponding to the intuitive notion, remains a challenging problem.