this paper, we combine different systems employing known representations. The observation that suggests this approach is that systems that are designed differently, either because they use a different formalism or because they contain different knowledge, will typically produce different errors. We hope to make use of this fact and reduce the number of errors with very little additional effort by exploiting the disagreement between different language models. Al- though the approach is applicable to any type of language model, we focus on the case of statistical disambiguators that are trained on annotated corpora. The examples of the task that are present in the corpus and its annotation are fed into a learning algorithm, which induces a model of the desired input-output mapping in the form of a classifier. * EO. Box 9103, 6500 HD Nijmegen, The Netherlands, hvh@let.ktm.nl t Universiteitsplein 1, 2610 Wilrijk, Belgium, {zavrel, daelem}@uia.ua.ac.be () 2000 Association for Computational Linguistics We use a number of different learning algorithms simultaneously on the same training corpus. Each type of learning method brings its own 'inductive bias' to the task and will produce a classifier with slightly different characteristics, so that different methods will tend to produce different errors

Abstract—By using other agents ’ experiences and knowledge, a learning agent may learn faster, make fewer mistakes, and create some rules for unseen situations. These benefits would be gained if the learning agent can extract proper rules out of the other agents’ knowledge for its own requirements. One possible way to do this is to have the learner assign some expertness values (intelligence level values) to the other agents and use their knowledge accordingly. In this paper, some criteria to measure the expertness of the reinforcement learning agents are introduced. Also, a new cooperative learning method, called weighted strategy sharing (WSS) is presented. In this method, each agent measures the expertness of its teammates and assigns a weight to their knowledge and learns from them accordingly. The presented methods are tested on two Hunter–Prey systems. We consider that the agents are all learning from each other and compare them with those who cooperate only with the more expert ones. Also, the effect of the communication noise, as a source of uncertainty, on the cooperative learning method is studied. Moreover, the Q-table of one of the cooperative agents is changed randomly and its effects on the presented methods are examined. Index Terms—Cooperative learning, expertness, multi-agent systems, Q-learning.

Combining unbiased forecasts of continuous variables necessarily reduces the error variance below that of the median individual forecast. However, this does not necessarily hold for forecasts of discrete variables, or where the costs of errors are not directly related to the error variance. This paper investigates empirically the benefits of combining forecasts of outperforming shares, based on five linear and nonlinear statistical classification techniques, including neural network and recursive partitioning methods. We find that simple “Majority Voting ” improves accuracy and profitability only marginally. Much greater gains come from applying the “Unanimity Principle”, whereby a share is not held in the high-performing portfolio unless all classifiers agree.

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The use of classifier combination has taken center stage in machine learning research, where outputs of different classifiers are combined in various ways to achieve a perceived better performance than that of any of the base classifiers involved in the combination. Many a research has however not empirically justified the use of the participating classifiers in a combination. This work looks at the usage of meta-knowledge that expresses the performance of each learning method on diverse domains to choose the most suitable learning algorithms suited for a combination for particular domains. The meta-knowledge is considered in this work is limited to the characteristics of the data involved. The approach works by having a learning algorithm that learns and describes how the data characteristics and the combined learning algorithms relate. Given a new learning domain, the domain characteristics are measured, and from the induced relationship, a selection of the most suitable base algorithms for combination will be done. The results of this work provide a fundamental step in achieving a cohesive framework for classifier combination.

this paper, we will follow the definition 1 for the term bioinformatics

Cooperative Learning in a multi-agent system can improve the learning quality and learning speed. The improvement can be gained if each agent detects the expert agents and use their knowledge properly. In this paper, a new cooperative learning method, called Weighted Strategy Sharing (WSS) is introduced. Also some criteria are introduced to measure the expertness of agents. In WSS, based on the amount of its teammate expertness, each agent assigns a weight to their knowledge. These weights are used in sharing knowledge among agents in our system. WSS and the expertness criteria are tested on two simulated Hunter-Prey problem and Object Pushing systems. 1

Abstract—A group of cooperative and homogeneous Q-learning agents can cooperate to learn faster and gain more knowledge. In order to do so, each learner agent must be able to evaluate the expertness and the intelligence level of the other agents, and to assess the knowledge and the information it gets from them. In addition, the learner needs a suitable method to properly combine its own knowledge and what it gains from the other agents according to their relative expertness. In this paper, some expertness measuring criteria are introduced. Also, a new cooperative learning method called weighted strategy sharing (WSS) is introduced. In WSS, based on the amount of its teammate expertness, each agent assigns a weight to their knowledge and utilizes it accordingly. WSS and the expertness criteria are tested on two simulated hunter–prey and object-pushing systems.