instance-based learning algorithm
Statistical Data Classification Using Instance Based Learning Algorithm
... KNN algorithm performs well with all required parametric ...efficient algorithm for storing the existing parameters and the new data is classified on grounds of similarity ...supervised learning [6]. ...
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Noise Reduction for Instance-Based Learning with a Local Maximal Margin Approach
... Instance Based (IB) Learning Algorithms (IBn), presented by Aha et al (1991), can be considered the first hybrid approaches to ...online learning method, similar to CNN, that works by adding ...
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Theoretical analyses of cross-validation error and voting in instance-based learning
... in instance-based ...typical instance-based learning algorithm predicts the desig- nated attribute by voting among the k nearest neighbors (the k most similar examples) to the ...
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Twin Support Vector Machine for Multiple Instance Learning Based on Bag Dissimilarities
... instance learning. Kotzias et al. have also combined the concept of deep learning and multi- instance learning for knowledge transfer ...multiple instance learning tasks ...
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Multi-instance multi-label learning
... Multi-Label learning) framework where an example is described by multiple instances and associated with multiple class ...traditional learning frameworks, the MIML framework is more convenient and natural ...
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Horror image recognition based on context aware multi instance learning
... multi-instance learning (CMIL) algorithm for horror image ...CMIL algorithm identifies horror images and picks out the regions that cause the sensation of horror in these horror ...procedure ...
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A meta-learning system for multi-instance classification
... This algorithm models the class conditional probability distributions of the ...distance based approaches such as the different versions of Diverse Density (MDD, MIEMDD and MIDD), MIOptimalBall, and ...
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Integrate Model and Instance Based Machine Learning for Network Intrusion Detection
... clustering algorithm to a more robust method underlying the fact that if the clusters are empty in initial iterations over-splitting may be avoided by merging the overlapping clusters, they called it as Y-Means ...
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Multiple-Instance Learning from Distributions
... multiple-instance learning (MIL) ...a learning algorithm in the form of la- beled sets, or “bags,” of ...an algorithm is to learn a function that correctly labels new bags or a function ...
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Multiple Instance Learning of Real Valued Data
... The above analysis makes many simplifying assumptions. However, even under these idealistic settings one can clearly see that in some situations k-NN, when using the minimal Hausdorff met- ric (which is quite different ...
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Learning Instance-Specific Predictive Models
... of instance-specific methods, and AB is an ensemble ...LBR algorithm induces a rule tailored to the features of the test instance that is then used to classify it, and is an example of a ...
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Multi-Instance Learning with Any Hypothesis Class
... supervised learning setting termed Multiple-Instance Learning (MIL), the examples are bags of instances, and the bag label is a function of the labels of its ...the instance labels that ...
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A review of multi-instance learning assumptions
... The task of enumerating all axis-parallel boxes is exponential in the number of dimensions, which makes GMIL-1 very inefficient. GMIL-2 Tao and Scott (2004) is an attempt to improve the computational and memory ...
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Machine Learning for Instance Segmentation
... Various authors demonstrated that learned boundary probabilities (or, more generally, boundary strengths) are superior to designed ones. In the most common setting, these probabilities are defined on the pixel grid, i.e. ...
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Multi-view multi-instance learning based on joint sparse representation and multi-view dictionary learning
... dictionary learning algorithm to obtain a multi-view graph dictionary that considers cues from all views simultaneously to improve the discrimination of the M 2 ...
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Multi view multi instance learning based on joint sparse representation and multi view dictionary learning
... dictionary learning algorithm to obtain a multi-view graph dictionary that considers cues from all views simultaneously to improve the discrimination of the M 2 ...
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Explaining the Stars: Weighted Multiple Instance Learning for Aspect Based Sentiment Analysis
... EM algorithm which assumes that one primary instance per bag is responsible for its ...timate instance weights per bag for crop yield modeling (not applicable to ...single- instance feature ...
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C PNN: Multiple instance Learning Based on Clustering and Probabilistic Neural Network
... single-instance learning(SIL) and multiple-instance learning(MIL), many traditional learning methods such as nearest neighbor classification, SVM and other classification algorithms are ...
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Class Balanced Similarity-Based Instance Transfer Learning for Botnet Family Classification
... transfer learning algorithm that is based on the similarity between instances from the target and source datasets (the SBIT ...the algorithm is aware of the percentage of classes in the ...
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Experiments in Graph Based Semi Supervised Learning Methods for Class Instance Acquisition
... For instance, the Freebase-1 graph has a high average degree of ...3). Based on this, we suggest that average degree, an easily computable struc- tural property of the graph, may be a useful indica- tor in ...
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