q-gram Based Database Searching Using a Suffix Array (QUASAR)
... from the database where the query sequence possibly occurs with a high level of similarity. These positions can later be inspected in more depth with a standard alignment algorithm. The crucial point in the approach we use for QUASAR was therefore the design of the lter which combines some already ...
... from the database where the query sequence possibly occurs with a high level of similarity. These positions can later be inspected in more depth with a standard alignment algorithm. The crucial point in the approach we use for QUASAR was therefore the design of the lter which combines some already ...
Mobile Robot Path Planning in Static Environments using Particle
... for solving the path planning problem has drawn the attention of researchers because of the advantages they offer, such as easy implementation, and fast generation of acceptable solution if there exists one. Particle swarm optimization is a very simple, yet a very powerful heuristic optimization tec ...
... for solving the path planning problem has drawn the attention of researchers because of the advantages they offer, such as easy implementation, and fast generation of acceptable solution if there exists one. Particle swarm optimization is a very simple, yet a very powerful heuristic optimization tec ...
Efficient quantum algorithms for some instances of the non
... whenever the group is Abelian. The main tool for this solution is the (approximate) quantum Fourier transform which can be efficiently implemented by a quantum algorithm [17]. Simon’s algorithm for finding an xor-mask [27], Shor’s seminal factorization and discrete logarithm finding algorithms [26], ...
... whenever the group is Abelian. The main tool for this solution is the (approximate) quantum Fourier transform which can be efficiently implemented by a quantum algorithm [17]. Simon’s algorithm for finding an xor-mask [27], Shor’s seminal factorization and discrete logarithm finding algorithms [26], ...
as a PDF
... have plausible neuronal implementations, but there is no associated claim, express or implied, that the brain actually performs those calculations. Put simply, BECCA’s purpose is not to describe the brain, but to perform like it. ...
... have plausible neuronal implementations, but there is no associated claim, express or implied, that the brain actually performs those calculations. Put simply, BECCA’s purpose is not to describe the brain, but to perform like it. ...
ECAI Paper PDF - MIT Computer Science and Artificial Intelligence
... × on it). The semiring operations (+ and ×) model constraint projection and combination, respectively. A subset of the variables, called type variables, specifies the variables to appear in the solutions. In this paper, we show how model-based diagnosis, and in general optimization problems composed ...
... × on it). The semiring operations (+ and ×) model constraint projection and combination, respectively. A subset of the variables, called type variables, specifies the variables to appear in the solutions. In this paper, we show how model-based diagnosis, and in general optimization problems composed ...
Sample Chapter
... extensive and pure knowledge. This part contains no control or programming information. The problem of semantics is resolved by representing the knowledge in proper structure. 5. Opacity: Along with the advantages, there are certain disadvantages also associated with production systems. Opacity is t ...
... extensive and pure knowledge. This part contains no control or programming information. The problem of semantics is resolved by representing the knowledge in proper structure. 5. Opacity: Along with the advantages, there are certain disadvantages also associated with production systems. Opacity is t ...
YAHSP3 and YAHSP3-MT in the 8th - Vincent Vidal
... analysis of relaxed plans. The core of the solver has nearly not evolved since IPC-2011 where YAHSP2 competed, and is described in full details in (Vidal 2011). It can be noted that a minor bug with major effects has been fixed, which prevented YAHSP2 to find valid plans in domains with Ocost action ...
... analysis of relaxed plans. The core of the solver has nearly not evolved since IPC-2011 where YAHSP2 competed, and is described in full details in (Vidal 2011). It can be noted that a minor bug with major effects has been fixed, which prevented YAHSP2 to find valid plans in domains with Ocost action ...
A Simulation Approach to Optimal Stopping Under Partial Information
... frameworks. When the transition density of the state variables is known, classical (a) dynamic programming computations are possible, see e.g. [38]. If the problem state is low-dimensional and Markov, one may alternatively use the quasi-variational formulation to obtain a free-boundary partial diffe ...
... frameworks. When the transition density of the state variables is known, classical (a) dynamic programming computations are possible, see e.g. [38]. If the problem state is low-dimensional and Markov, one may alternatively use the quasi-variational formulation to obtain a free-boundary partial diffe ...
Feature Selection Using Fuzzy Objective Functions
... for data mining is feature selection. Real-world data analysis, data mining, classification and modeling problems usually involve a large number of candidate inputs or features. Less relevant or highly correlated features decrease, in general, the classification accuracy, and enlarge the complexity ...
... for data mining is feature selection. Real-world data analysis, data mining, classification and modeling problems usually involve a large number of candidate inputs or features. Less relevant or highly correlated features decrease, in general, the classification accuracy, and enlarge the complexity ...
Genetic algorithm
In the field of artificial intelligence, a genetic algorithm (GA) is a search heuristic that mimics the process of natural selection. This heuristic (also sometimes called a metaheuristic) is routinely used to generate useful solutions to optimization and search problems. Genetic algorithms belong to the larger class of evolutionary algorithms (EA), which generate solutions to optimization problems using techniques inspired by natural evolution, such as inheritance, mutation, selection, and crossover.