[PDF] Top 20 Learning Kernels for Semantic Clustering: A Deep Approach
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Learning Kernels for Semantic Clustering: A Deep Approach
... of semantic clusters can’t be defined a priory (un- like to ...treating semantic spaces in the sense of stability of algorithms (Vap- nik, 1998), ...of semantic similarity consistence, although ... See full document
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Deep Learning in Semantic Kernel Spaces
... such kernels can be ap- plied only on vector inputs. In Yu et al. (2009), deep neural networks for rapid visual recognition are trained with a novel regularization method tak- ing advantage of ... See full document
10
Deep Feature Learning for Medical Image Analysis for Detection of Brain Tumor
... Presented approach of Spatial Fuzzy C means (PET-SFCM) clustering technique on Positron Emission Tomography (PET) scan image datasets. Proposed technique is included the spatial neighborhood information ... See full document
8
Recognizing Textual Entailment based on Deep Learning Approach
... active learning strategies to address the lack of labeled ...active learning experiments. Deep learning neural networks which convert the input data into different representation vectors is ... See full document
6
Deep Learning of Binary and Gradient Judgements for Semantic Paraphrase
... This approach is different from the one adopted in semantic similarity datasets, where a pair of words or sentences is labeled on a gradient classification ...cases, semantic similarity tasks overlap ... See full document
9
Recognizing UMLS Semantic Types with Deep Learning
... Entity recognition is a critical first step to a number of clinical NLP applications, such as entity linking and relation extraction. We present the first attempt to apply state-of-the- art entity recognition approaches ... See full document
11
Semantic Segmentation using Deep Learning
... Abstract— Semantic image segmentation is an essential com- ponent of modern autonomous driving systems, as an accurate understanding of the surrounding scene is crucial to navigation and action ...in ... See full document
10
Modelling input texts: from Tree Kernels to Deep Learning
... applying kernels on datasets with millions of ...(RKE) approach [113], which exploits SVM models to extract the most important features from the kernel space; and (vi) recent advances in fast SVM ... See full document
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A Survey on Deep Feature Learning For Medical Image Analysis for Detection of Brain Tumor
... Presented approach of Spatial Fuzzy C means (PET-SFCM) clustering technique on Positron Emission Tomography (PET) scan image datasets. Proposed technique is included the spatial neighborhood information ... See full document
5
Latent Semantic Kernels
... new approach to solving machine learning ...this approach is its modularity: the decoupling of algorithm design and statistical analysis from the problem of creating appropriate function/feature ... See full document
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Semantic indexing with deep learning: a case study
... Coarse classification is a multi-class classification problem in our model, and this layers labels are independent of each other; that is, each document belongs to one of these major categories. The category number (C) ... See full document
13
Deep Multitask Learning for Semantic Dependency Parsing
... apply deep multitask learning to graph-based ...state-of-the-art semantic dependency pars- ing systems that use ...formalisms, semantic or ... See full document
12
A Personalized Markov Clustering and Deep Learning Approach for Arabic Text Categorization
... stage approach to classify Arabic text documents into different categories combining Markov Cluster- ing, Fuzzy-C-means and Deep ...both clustering and deep learning to perform ... See full document
7
Learning Scalable Deep Kernels with Recurrent Structure
... the approach and demonstrated strong results in regression and classification tasks for kernels based on feedforward and convolutional ...learn kernels with recurrent structure by transforming input ... See full document
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Semantic Kernels for Semantic Parsing
... tree kernels: (i) Partial Tree Kernel (PTK), which can be effectively applied to both constituency and dependency parse trees (Moschitti, ...or semantic kernel (SK) (Croce et ...based clustering ... See full document
7
An investigation on the Vocational High School Students' Learning Approaches in Terms of Various Variables
... active learning process, the question “How do individuals learn?” has become the focus point as well as “How are they ...of learning approach together. Learning approach concept was ... See full document
6
Verb Classification using Distributional Similarity in Syntactic and Semantic Structures
... available; and (ii) in 76% of the errors only 2 or less argument heads are included in the extracted tree, therefore tree kernels cannot exploit enough lexical information to disambiguate verb senses. Addition- ... See full document
10
Tree Kernels for Semantic Role Labeling
... However, Row Poly shows that the polynomial kernel using state-of-the-art fea- tures (Moschitti et al. 2005b) outperforms AST m 1 by about 4.5 percentage points in BD and 8 points in the SRL task. The main reason is that ... See full document
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Semantic Classification with Distributional Kernels
... Distributional kernels on strings and trees should provide a flexible implementation of these suggestions that is compatible with SVM classification and does not require manual feature ...to kernels on sets ... See full document
8
Learning principles of accounting in ICT-supported learning environments of Malaysian secondary schools: future-oriented approach
... in learning approaches. The surface approach represents learning through memorising to meet the minimum requirement in assessment, while the deep approach stresses the importance of ... See full document
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