feature space
Incremental Feature Subsetting useful for Big Feature Space Problems
... Tracking the pattern, reducing noise and narrowing down on the highly variant features [45] in practical applications such as radar, sonars etc can prove to be very useful. Each sensor will have its own domain in scope ...
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Object Classification and Detection in High Dimensional Feature Space
... In this thesis we studied the problem of learning and detecting objects in high dimensional feature space. In the first part, we showed that integrating information about the division of the feature ...
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Restricting Supervised Learning:Feature Selection and Feature Space Partition
... dimensional space, suffer the curse of dimensionality, and there are not enough observations to obtain good ...the feature space into multiple non-overlapping regions such that each region is simple ...
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An Optimal Temporal and Feature Space Allocation in Supervised Data Mining
... dependency, and simply treat as cross sectional data, i.e., the order of records does not affect the performance. If the time dependency structure is also considered in the mining scheme development, we are interested in ...
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Improving pairwise coreference models through feature space hierarchy learning
... the feature space (imagine taking the direct sum of all feature spaces), the more we get details on the distribution of mention pairs and the more we can expect to separate positives and neg- atives ...
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Predicting and explaining behavioral data with structured feature space decomposition
... a feature on the outcome variable through inspection of the linear coefficient of the feature in the model, but such analysis assumes independence of the features (so that other features can remain constant ...
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Automatic Speech Emotion Recognition- Feature Space Dimensionality and Classification Challenges
... size feature set relative to emotion, and consequently suggest the use of a large number of features in SER ...meta-feature space instead of ...emotional space adaptation from speaker ...
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An Image Based Feature Space and Mapping for Linking Regions and Words
... The method has been applied to the Washington im- age set 1 which contains 697 semantically annotated images. After the original keyword labels were pro- cessed by correcting mistakes and merging plurals into singular ...
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Interactive Feature Space Construction using Semantic Information
... the feature space and model are specified before learn- ing begins and remain static throughout learning, where training data is exclusively used for parameter ...interactive feature space ...
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A Feature Space-based Business Model Quality Evaluation Method
... extended feature modeling technique is ...of feature space as the ...model, feature space, model quality evaluation, completeness, ...
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Supervised Feature Space Reduction for Multi-Label Nearest Neighbors
... Abstract. With the ability to process many real-world problems, multi- label classification has received a large attention in recent years and the instance-based ML-kNN classifier is today considered as one of the most ...
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The Gaussian Orthogonal Laplacianfaces Modelling in Feature Space for Facial Image Recognition
... the feature space on the first stage of the Kernel Laplacianfaces method, so that the second, third, and fourth stages also differ from the Orthogonal Laplacianfaces ...the feature space, ...
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Evaluation of Feature Space Speaker Adaptation for End to End Acoustic Models
... adaptation techniques to end-to-end AMs. In (Miao et al., 2016), vocal tract length normalization (VTLN) (Lee and Rose, 1996) has been applied to filterbank features, for a neural end-to-end AM training through ...
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Feature space analysis for human activity recognition in smart environments
... A number of different techniques have been proposed for activity recognition or classification [1], based for examples on Hidden Markov Models, Conditional Random Fields [9], Support Vector Machines [4], ontologies [10] ...
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Dark endmember in spectral feature space under variable atmosphere
... 2-D feature space show that the spectral response under decreasing illumination condition tends to converge at the origin with very strong linear relationship from both instruments, defined as shadow-line ...
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Nonlinear Processes in Geophysics Nonlinear multidimensional scaling and visualization of earthquake clusters over space, time and feature space
... The entire time interval in the feature space has been di- vided into two parts. The events from the first 2/3 of the in- terval – approximately 700 events – represent the “teaching set”, the rest – about ...
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A Closer Look At Feature Space Data Augmentation For Few Shot Intent Classification
... For IC, we finetune a pre-trained English BERT- Base uncased model 1 to build our feature extrac- tor. The BERT model has 12 layers, 768 hidden states, and 12 heads. We use the pooled represen- tation of the ...
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Variable Selection Method for Aluminum Electrolytic Process Based on FNN and RM in KPLS Feature Space
... KPLS feature space is ...original space to the PLS feature space; then, in the new feature space, the FNN is employed to calculate the similarity measure of process ...
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Memory-guided tracking through physical space and feature space
... color space as well as number space, while Experiment 4 found modest evidence for similarities between color and number ...physical space and feature ...
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Spatio-Temporal Anomaly Detection for Industrial Robots through Prediction in Unsupervised Feature Space
... learned feature space (Figure 3). As the feature space is an encoding of the original image space, the difference between the prediction and actual observa- tion can reveal ...the ...
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