Learning in the Feature Space
Multiple Kernel Learning and Feature Space Denoising
... kernel learning (MKL) problem was pioneered by Lancriet et ...machine learning com- munity in the past few ...base feature spaces. We ar- gue that in the case where the base feature spaces are ...
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Improving pairwise coreference models through feature space hierarchy learning
... In this paper, we claim that mention pairs should not be processed by a single classifier, and instead should be handled through specific mod- els. But we are furthermore interested in learning how to construct ...
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Improving pairwise coreference models through feature space hierarchy learning
... In this paper, we claim that mention pairs should not be processed by a single classifier, and instead should be handled through specific mod- els. But we are furthermore interested in learning how to construct ...
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Transfer learning by feature-space transformation: A method for Hippocampus segmentation across scanners
... a feature-space transformation (FST), since it maps the entire feature space of a training image to that of a test ...the feature- space transformation from images of subjects ...
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Educational Data Clustering in a Weighted Feature Space Using Kernel K-Means and Transfer Learning Algorithms
... transfer learning techniques, a transfer-learning-based clustering method is defined with the kernel k-means and spectral feature alignment algorithms in our paper as a solution to the educational ...
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Restricting Supervised Learning:Feature Selection and Feature Space Partition
... supervised learning problems are considered difficult to solve either because of the redundant features or because of the structural complexity of the generative ...the learning noise and therefore decrease ...
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Segmentation of Multiple Sclerosis Lesions Using Dictionary Learning in Feature Space
... new feature space used in ...these feature spaces that further investigation could result in a more efficient means of automatic ...of feature extraction became very time ...
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Gesture-Timbre Space: Multidimensional Feature Mapping Using Machine Learning & Concatenative Synthesis
... generate feature mapping using one corpus of sounds, and then either augment that corpus or change to an entirely different corpus – moving the timbral trajectory of a gesture space into a new set of ...
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Visual Feature Learning
... different feature spaces to obtain a single linear/non-linear subspace, thus it affords an efficient eigenvalue based solution, and it is applicable to be extended to the cross-modality ...a learning system ...
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From Feature Space to Primal Space: KPCA and Its Mixture Model
... machine learning. The definition of learning is broad enough to include most tasks that we commonly call “learning” tasks, as we use the word in daily ...
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Invariant Integration in Deep Convolutional Feature Space
... 1 Introduction Deep neural networks (DNNs) are the state-of-the-art method to solve a wide variety of complex tasks from computer vision to speech recognition. In gen- eral, the weights of those networks are optimized ...
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Mining recurring concepts in a dynamic feature space
... 2) meta-learning level where: a) detection and adaptation to concept changes; b) the context-concept relations are learnt and used to deal with the recurring concepts; and c) classify[r] ...
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Design of Interactive Feature Space Construction Protocol
... active learning provides a way to reduce the labeling costs by labeling only the most useful instances for ...supervised learning, active learning and learning for complex ...of ...
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Feature Space Augmentation for Long-Tailed Data
... the feature space using the information from the head ...high-level feature space due to a more “linear” representation at that ...such feature space augmen- tation, and ...
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A New Feature Selection Technique Combined with ELM Feature Space for Text Classification
... Extreme Learning Machine (ELM) is able to ap- proximate any complex non-linear mappings di- rectly from the training samples (Huang et ...quick learning speed, ability to manage huge volume of data, re- ...
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Multiplicative Multitask Feature Learning
... Research efforts have been devoted to various MultiTask Feature Learning (MTFL) algo- rithms. One direction of these works directly learns the dependencies among tasks, either by modeling the correlated ...
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Interactive Feature Space Construction using Semantic Information
... machine learning solutions is to reduce the man- ual effort required to achieve state of the art perfor- ...automate feature engineering through an interactive learning ...
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Object Classification and Detection in High Dimensional Feature Space
... of learning and detecting objects in high dimensional feature ...the feature space into homogeneous subsets can reduce the training time ...by feature responses for instance with a ...
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Transfer learning in hierarchical feature spaces
... Keywords-transfer learning, deep learning, feature extraction ...same feature space and distribution. It means that if the feature space or/and distribution of the target ...
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K-Means Clustering in Dual Space for Unsupervised Feature Partitioning in Multi-view Learning
... Multi-view learning is typically used when the data are described by a large number of ...multi-view learning – in the case where the features have no natural groupings – is multi- view generation (MVG): it ...
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