Martikán, Miroslav · Brno University of Technology Digital Library (Brno University of Technology) · 2009
Tato práce je zaměřena na algoritmy učení založeného na instancích. Hlavním cílem je vytvoření výukové aplikace. V práci jsou teoreticky popsány algoritmy učení založeného na instancích (IBL), metoda nejbližšího souseda a kd-stromy. Praktická část je věnovaná tvorbě aplikace pro výuku. Aplikace umožňuje generování množin dat, jejich klasifikaci metodou nejbližšího souseda a testování algoritmů IB1, IB2 a IB3.
Cited by 0
Karl Branting, Patrick Broos · 1994
This paper proposes a new algorithm for acquisition of preference predicates by a learning apprentice, termed Compositional Instance-Based Learning (CIBL), that permits multiple instances of a preference predicate to be composed, directly exploiting the transitivity of preference predicates. In an empirical evaluation, CIBL was consistently more accurate than a 1-NN instance-based learning strategy unable to compose instances. The relative performance of CIBL and decision tree induction was found to depend upon (1) the complexity of the preference predicate being acquired and (2) the dimensionality of the feature space.
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Eyke Hüllermeier · Artificial Intelligence · 2003
Cited by 35
Qiang Yang, Yu Zhang, Wenyuan Dai et al. · Cambridge University Press eBooks · 2020
Cited by 1
David W. Aha, Dennis Kibler, Marc K. Albert · Machine Learning · 1991
Cited by 2,901Open access
SpringerReference · 2012
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David W. Aha, Dennis Kibler, Marc K. Albert · Machine Learning · 1991
Cited by 4,093Open access
William D. Smart · Encyclopedia of Machine Learning and Data Mining · 2017
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Viktor Zielke · 2013
The discovery of possible interactions with objects is a vital part of an exploration task for robots. An important subset of these possible interactions are affordances. Affordances describe what a specific object can afford to specific agent, based on the capabilities of the agent and the properties of the object in relation to the agent. For example, a chair affords a human to be sat-upon, if the sitting area of the chair is approximately knee-high. In this work, an instance-based learning approach is made to discover these affordances solely through different visual representations of point cloud data of an object. The point clouds are acquired with a Microsoft Kinect sensor. Different representations are tested and evaluated against a set of point cloud data of various objects found in a living room environment.
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Dutt, Varun, Gonzalez, Cleotilde · KiltHub Repository · 2018
This paper focuses on the creation and presentation of a user-friendly experience for developing computational models of human behavior. Although computational models of human behavior have enjoyed a rich history in cognitive psychology, they have lacked widespread impact, partly due to the technical knowledge and programming required in addition to the complexities of the modeling process. We describe a modeling tool called IBLTool that is a computational implementation of the Instance-based Learning Theory (IBLT). IBLT is a theory that represents how decisions are made from experience in dynamic tasks. The IBLTool makes IBLT usable and understandable to a wider community of cognitive and behavioral scientists. The tool uses graphical user interfaces that take a modeler step-by-step through several IBLT processes and help the modeler derive predictions of human behavior in a particular task. A task would connect and interact with the IBLTool and store the decision-making data while the tool collects statistical data from the execution of a model for the task. We explain the functioning of the IBLTool and demonstrate a concrete example of the design and execution of a model for the Iowa Gambling task. The example is intended to provide a concrete demonstration of the capabilities of the IBLTool.
Cited by 0Open access
Dutt, Varun, Gonzalez, Cleotilde · Figshare · 2018
This paper focuses on the creation and presentation of a user-friendly experience for developing computational models of human behavior. Although computational models of human behavior have enjoyed a rich history in cognitive psychology, they have lacked widespread impact, partly due to the technical knowledge and programming required in addition to the complexities of the modeling process. We describe a modeling tool called IBLTool that is a computational implementation of the Instance-based Learning Theory (IBLT). IBLT is a theory that represents how decisions are made from experience in dynamic tasks. The IBLTool makes IBLT usable and understandable to a wider community of cognitive and behavioral scientists. The tool uses graphical user interfaces that take a modeler step-by-step through several IBLT processes and help the modeler derive predictions of human behavior in a particular task. A task would connect and interact with the IBLTool and store the decision-making data while the tool collects statistical data from the execution of a model for the task. We explain the functioning of the IBLTool and demonstrate a concrete example of the design and execution of a model for the Iowa Gambling task. The example is intended to provide a concrete demonstration of the capabilities of the IBLTool.
Cited by 0Open access
Varun Dutt, Cleotilde González · Computers in Human Behavior · 2012
Cited by 29
Viktor Zielke · OPUS Publication Server of the University of Stuttgart (University of Stuttgart) · 2014
The discovery of possible interactions with objects is a vital part of an exploration task for robots. An important subset of these possible interactions are affordances. Affordances describe what a specific object can afford to a specific agent, based on the capabilities of the agent and the properties of the object in relation to the agent. For example, a chair affords a human to be sat-upon, if the sitting area of the chair is approximately knee-high. In this work, an instance-based learning approach is made to discover these affordances solely through different visual representations of point cloud data of an object. The point clouds are acquired with a Microsoft Kinect sensor. Different representations are tested and evaluated against a set of point cloud data of various objects found in a living room environment.
Cited by 0Open access
Ashish Shrivastava, Jaishanker K. Pillai, Vishal M. Patel et al. · Zenodo (CERN European Organization for Nuclear Research) · 2014
We present a multi-class, multiple instance learning (MIL) algorithm using the dictionary learning framework where the data is given in the form of bags. Each bag contains multiple samples, called instances, out of which at least one belongs to the class of the bag. We propose a noisy-OR model-based optimization framework for learning the dictionaries. Our method can be viewed as a generalized dictionary learning algorithm since it reduces to a novel discriminative dictionary learning framework when there is only one instance in each bag. Various experiments using the popular MIL datasets show that the proposed method performs better than existing methods.
Cited by 15Open access
Tianyang Wang, Jun Huan, Michelle Zhu · arXiv (Cornell University) · 2018
Deep transfer learning recently has acquired significant research interest. It makes use of pre-trained models that are learned from a source domain, and utilizes these models for the tasks in a target domain. Model-based deep transfer learning is probably the most frequently used method. However, very little research work has been devoted to enhancing deep transfer learning by focusing on the influence of data. In this paper, we propose an instance-based approach to improve deep transfer learning in a target domain. Specifically, we choose a pre-trained model from a source domain and apply this model to estimate the influence of training samples in a target domain. Then we optimize the training data of the target domain by removing the training samples that will lower the performance of the pre-trained model. We later either fine-tune the pre-trained model with the optimized training data in the target domain, or build a new model which is initialized partially based on the pre-trained model, and fine-tune it with the optimized training data in the target domain. Using this approach, transfer learning can help deep learning models to capture more useful features. Extensive experiments demonstrate the effectiveness of our approach on boosting the quality of deep learning models for some common computer vision tasks, such as image classification.
Cited by 1Open access
Pak-Ming Cheung · 2006
Kernel-based multiple-instance learning by Pak-Ming Cheung thesis 2006 x, 47 leaves : ill. ; 30 cm In recent years, the Multiple-Instance Learning (MIL) problem is becoming more and more popular in the machine learning community. Each training object (bag) of…Read more ›
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Matthias Fuchs · 2002
Instance based learning (IBL) methods fascinate with their conceptual simplicity. Usually, IBL systems center on (a subset of) given "training" instances. Restricting the acquisition of instances to given training instances is known to cause difficulties and limitations. We propose to overcome these limitations by searching for suitable instances. We employ a genetic algorithm to conduct such an intricate search. We demonstrate the viability of this approach in connection with instance based concept learning.
Cited by 1
Rare & Special e-Zone (The Hong Kong University of Science and Technology) · 2006
Kernel-based multiple-instance learning by Pak-Ming Cheung thesis 2006 x, 47 leaves : ill. ; 30 cm In recent years, the Multiple-Instance Learning (MIL) problem is becoming more and more popular in the machine learning community. Each training object (bag) of…Read more ›
Cited by 0
Henry Brighton, Chris Mellish · 2002
The basic nearest neighbour classifier suffers from the indiscriminate storage of all presented training instances. With a large database of instances classification response time can be slow. When noisy instances are present classification accuracy can suffer. Drawing on the large body of relevant work carried out in the past 30 years, we review the principle approaches to solving these problems. By deleting instances, both problems can be alleviated, but the criterion used is typically assumed to be all encompassing and effective over many domains. We argue against this position and introduce an algorithm that rivals the most successful existing algorithm. When evaluated on 30 different problems, neither algorithm consistently outperforms the other: consistency is very hard. To achieve the best results, we need to develop mechanisms that provide insights into the structure of class definitions. We discuss the possibility of these mechanisms and propose some initial measures that could be useful for the data miner.
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Muhammad Waqas, Rizwan Qureshi · 2021
The Contribution to the 9th European Workshop on Visual Information Processing. The work is focuses on instance relevance estimation process in domain of multiple instance learning. <br>
Cited by 2Open access