نتایج جستجو برای: minimal learning parameters algorithm
تعداد نتایج: 1881512 فیلتر نتایج به سال:
In this work, we propose a new fuzzy reinforcement learning algorithm for differential games that have continuous state and action spaces. The proposed algorithm uses function approximation systems whose parameters are updated differently from the updating mechanisms used in the algorithms proposed in the literature. Unlike the algorithms presented in the literature which use the direct algorit...
Reinforcement learning is suitable for navigation of a mobile robot due to its learning ability without supervised information. Reinforcement learning, however, has difficulties. One is its slow learning, and the other is the necessity of specifying its parameter values without prior information. We proposed to introduce sensory signals into reinforcement learning to improve its learning perfor...
Community detection is a challenging optimization problem that consists of searching for communities that belong to a network under the assumption that the nodes of the same community share properties that enable the detection of new characteristics or functional relationships in the network. Although there are many algorithms developed for community detection, most of them are unsuitable when ...
In this project I attempt to learn open loop grasping parameters by implementing a two stage Gaussian Process (GP) based Upper Confidence Bound bandit solving algorithm. Through the course of the project, parametrized grasping strategies were developed, grasp verification using soft fingers via tandem grasp was developed, the bandit algorithm to exploit minimal number of robot interactions and ...
Appropriate bias is widely viewed as the key to e cient learning and generalization I present a new algorithm the Incremental Delta Bar Delta IDBD algorithm for the learning of appropri ate biases based on previous learning experience The IDBD algorithm is developed for the case of a simple linear learning system the LMS or delta rule with a separate learning rate parameter for each input The I...
The article suggests an algorithm for regular classifier ensemble methodology. The proposed methodology is based on possibilistic aggregation to classify samples. The argued method optimizes an objective function that combines environment recognition, multi-criteria aggregation term and a learning term. The optimization aims at learning backgrounds as solid clusters in subspaces of the high...
General deformable models have reduc edthe need for hand crafting new models for every new problem. But still most of the general models rely on manual inter action by an expert, when applied to a new problem, e.g., for selecting parameters and initialization. In this pap er we prop ose a full and uni ed scheme for applying the general deformable template model proposed by Grenander et al. [7, ...
optimizing the database queries is one of hard research problems. exhaustive search techniques like dynamic programming is suitable for queries with a few relations, but by increasing the number of relations in query, much use of memory and processing is needed, and the use of these methods is not suitable, so we have to use random and evolutionary methods. the use of evolutionary methods, beca...
An efficient genetic reinforcement learning algorithm for designing Fuzzy Inference System (FIS) with out any priory knowledge is proposed in this paper. Reinforcement learning using Fuzzy Q-Learning (FQL) is applied to select the consequent action values of a fuzzy inference system, in this method, the consequent value is selected from a predefined value set which is kept unchanged during lear...
Bayesian networks have gained increasing attention in recent years. One key issue in Bayesian networks (BNs) is parameter learning. When training data is incomplete or sparse or when multiple hidden nodes exist, learning parameters in Bayesian networks (BNs) becomes extremely difficult. Under these circumstances, the learning algorithms are required to operate in a high-dimensional search space...
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