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We propose using multi-layer multiple instance learning (MMIL) for image set classification and applying it to the task of cannabis website classification. We treat each image as an instance in an image set, then each image is further viewed as containing instances of local image patches. This representation naturally extends traditional multiple instance learning (MIL) to multi-layers. We then show...
If we imagine a dynamic environment whose behavior may change in time we can figure out the difficulties that agents located there will have trying to solve problems related to this environment. Changes in an environment e.g. a market, can be quite drastic: from changing the dependencies of some products to add new actions to build new products. The agents should try to cooperate or compete against...
In this paper, we propose a Relation Expansion framework, which uses a few seed sentences marked up with two entities to expand a set of sentences containing target relations. During the expansion process, label propagation algorithm is used to select the most confident entity pairs and context patterns. The label propagation algorithm is a graph based semi-supervised learning method which models...
While much research has been performed on query logs collected for major Web search engines, query log analysis to enhance search on smaller and more focused collections has attracted less attention. Our hypothesis is that an intranet search engine can be enhanced by adapting the search system to real users' search behaviour through exploiting its query logs. In this work we describe how a constantly...
Our goal in this paper was to investigate the impact that the addition of a Heterogeneous layered Social network will have on problem solving ability of a Cultural system. The synergisms of the emergent swarms in the population and belief spaces are affected by training the social network on a dynamic but recurring pattern weaved by our social influence function. The cultural system has adjusted its...
There has been growing interest in creating intelligent agents in virtual worlds that do not follow fixed scripts predefined by the developers, but react accordingly based on actions performed by human players during their interaction. In order to achieve this objective, previous approaches have attempted to model the environment and the user's context directly. However, a critical component for enabling...
This paper describes an interactive tool for constrained clustering that helps users to select effective constraints efficiently during the constrained clustering process. This tool has some functions such as 2-D visual arrangement of a data set and constraint assignment by mouse manipulation. Moreover, it can execute distance metric learning and k-medoids clustering. In this paper, we show the overview...
Research on opinion detection has shown that a large number of opinion-labeled data are necessary for capturing subtle opinions. However, opinion-labeled data, especially at the sub-document level, are often limited. This paper describes the application of Semi-Supervised Learning (SSL) to automatically produce more labeled data and explores the potential of SSL to improve transfer of labeled data...
With the rapid development of XML language which has good flexibility and interoperability, more and more log files of software running information are represented in XML format, especially for Web services. Fault diagnosis by analyzing semi-structured and XML like log files is becoming an important issue in this area. For most related learning methods, there is a basic assumption that training data...
In this study, under multi-agent environment, we introduce a notion of probabilistic Nash equilibrium into reinforcement learning method. Here we take an approach of mixed Nash strategy based on correlated technique in terms of Local Effect functions. We examine some experiment results to show some ideal properties for cooperative approach.
Majority of the existing approaches to service composition, including the widely popular planning based techniques, are not able to automatically compose practical workflows that include complex repetitive behaviors (loops), taking into account possibility of failures and non-determinism of web service execution results. In this work, we present a learning based approach for composing task specific...
Search engines have greatly influenced the way people access information on the Internet as such engines provide the preferred entry point to billions of pages on the Web. Therefore, highly ranked web pages generally have higher visibility to people and pushing the ranking higher has become the top priority for webmasters. As a matter of fact, search engine optimization (SEO) has became a sizeable...
Personalization, a topmost concern of modern recommendation systems (RS), is intended to predict individual motivation of a customer for this or that choice. It depends on many factors forming explicit and implicit decision context. The paper proposes RS personalization technology that focuses on ontology-based extraction of semantically interpretable context of each particular customer's decisions...
This paper presents a new type of digital repositories based on a hybrid organizational structure that leverages the potential of domain specific collaborative tagging in combination with a taxonomy-driven classification. It is exemplified in Linked Course - a repository prototype for collaborative development, sharing and reuse of learning resources equipped with adequate searching tools. The focus...
This article deals with the issue of concept learning and tries to have a game theoretic view over the process of cooperative concept learning among agents in a multi-agent system, in which an extreme sense of competition has arisen. This gives birth to a new realm labeled as ”Learning Games”. We study the cooperative view and give a novel idea to use in competitive environments based on the solution...
In this paper we propose a mechanism of prediction of domestic human activity in a smart home context. We use those predictions to adapt the behavior of home appliances whose impact on the environment is delayed (for example the heating). The behaviors of appliances are built by a reinforcement learning mechanism. We compare the behavior built by the learning approach with both a merely reactive behavior...
In this paper, we propose a cooperative learning algorithm for Multi-category classification which is decomposed into two sub-optimization problems by using the support vector machine technique. The proposed cooperative learning algorithm consists of two single learning algorithms and each sub-optimization problem is solved by one of them. Unlike the cooperative neural network, the proposed cooperative...
This paper presents an extension to the Rule-Based Similarity (RBS) model a novel rough set approach to the problem of learning a similarity relation from data. The original model, proposed in [1], applied the notion of Tversky's feature contrast model in a rough set framework to facilitate an accurate case-based classification. In the dynamic RBS model, a dynamic reducts technique is used to broaden...
User Navigation Behavior Mining (UNBM) mainly studies the problems of extracting the interesting user access patterns from user access sequences (UAS), which are usually used for user access prediction and web page recommendation. Through analyzing the real world web data, we find most of user access sequences carrying hybrid features of different patterns, rather than a single one. Therefore, the...
Collaborating agents require either prior agreement on the shared vocabularies that they use for communication, or some means of translating between their private ontologies. Thus, techniques that enable agents to build shared vocabularies allow them to share and learn new concepts, and are therefore beneficial when these concepts are required on multiple occasions. However, if this is not carried...
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