The proceedings contain 23 papers. The topics discussed include: on the usefulness of similarity based projection spaces for transfer learning;metric anomaly detection via asymmetric risk minimization;one shot similar...
ISBN:
(纸本)9783642244704
The proceedings contain 23 papers. The topics discussed include: on the usefulness of similarity based projection spaces for transfer learning;metric anomaly detection via asymmetric risk minimization;one shot similarity metric learning for action recognition;on a non-monotonicity effect of similarity measures;section-wise similarities for clustering and outlier detection of subjective sequential data;hybrid generative-discriminative nucleus classification of renal cell carcinoma;multi-task regularization of generative similarity models;a generative dyadic aspect model for evidence accumulation clustering;an information theoretic approach to learning generative graph prototypes;impact of the initialization in tree-based fast similarity search techniques;and multiple-instance learning with instance selection via dominant sets.
The proceedings contain 8 papers. The topics discussed include: multiple evaluation in future population distribution for sustainable city;detecting street signs in cities based on object recognition with machine lear...
ISBN:
(纸本)9781450360395
The proceedings contain 8 papers. The topics discussed include: multiple evaluation in future population distribution for sustainable city;detecting street signs in cities based on object recognition with machinelearning and GIS spatial analysis;D-record: disaster response and relief coordination pipeline;analysis, integration and visualization of urban data from multiple heterogeneous sources;SCOUTS: a smart community centric urban heat monitoring framework;real-time traffic light detection from videos with inertial sensor fusion;and an integrated visual analytics framework for spatiotemporal data.
In this paper we investigate a trie-based APRIORI algorithm for mining frequent item sequences in a transactional database. We examine the datastructure, implementation and algorithmic features mainly focusing on tho...
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ISBN:
(纸本)1595932100
In this paper we investigate a trie-based APRIORI algorithm for mining frequent item sequences in a transactional database. We examine the datastructure, implementation and algorithmic features mainly focusing on those that also arise in frequent itemset mining. In our analysis we take into consideration modern processors' properties (memory hierarchies, prefetching, branch prediction, cache line size, etc.), in order to better understand the results of the experiments. Copyright 2005 ACM.
Since the status of carbon emission indicators involves many factors, there is often a problem of large errors when evaluating them. For this reason, this paper proposes a study on a carbon emission indicator evaluati...
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ISBN:
(纸本)9798400707032
Since the status of carbon emission indicators involves many factors, there is often a problem of large errors when evaluating them. For this reason, this paper proposes a study on a carbon emission indicator evaluation method that integrates machinelearning and multi-modal data. After comprehensively analyzing the influencing factors of the value of carbon emission rights from the four perspectives of macroeconomics, energy prices, climate change, policy, and the international carbon market, we integrated multi-modal influencing factor data, calculated the corresponding carbon emission costs, and combined them as the input characteristic parameter of the BP neural network, through forward propagation and backward propagation, the linear relationship between it and the value of carbon emission rights is determined, and the corresponding model is constructed to realize the value evaluation of carbon emission rights. In the test results, the fit between the carbon emission rights value assessment results and the actual transaction price has always remained relatively stable, and the specific error has always been within 0.70 yuan/ton, with the maximum error being only 0.66 yuan/ton. Compared with the control group, it has obvious advantages in assessment accuracy and effectiveness respectively.
The proceedings contain 8 papers. The topics discussed include: towards multi-facet snippets for dataset search;towards employing semantic license annotations for sensor data profiling;miningmachine-readable knowledg...
The proceedings contain 8 papers. The topics discussed include: towards multi-facet snippets for dataset search;towards employing semantic license annotations for sensor data profiling;miningmachine-readable knowledge from structured web markup;using semantic domain-specific dataset profiles for data analytics;on the role of knowledge graphs in explainable AI;semantic web technologies for explainable machinelearning models: a literature review;towards automatic domain classification of LOV vocabularies;and semantic web technologies for explainable machinelearning models: a literature review.
Assessing the lower limb motor states of stroke patients based on biosignals is very important in the field of medical rehabilitation, and the importance of finding effective physiological signal indicators and proces...
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ISBN:
(纸本)9798400707032
Assessing the lower limb motor states of stroke patients based on biosignals is very important in the field of medical rehabilitation, and the importance of finding effective physiological signal indicators and processing methods for patient rehabilitation training and evaluation is self-evident. In this paper, a CNN-SVM model is constructed based on the CNN classifier to classify the three motion states of the lower limbs of the subjects, and the method is verified in the WAY-EEG-GAL multimodal open dataset to have a better classification effect, and the experimental data are used to verify the effectiveness of the model classification. The results show that the CNN-SVM method proposed in this paper outperforms the CNN classification model for all three classifications on both the WAY-EEG-GAL dataset and the experimental data, with average accuracies of 86.60% and 95.43%, respectively. This study provides a theoretical basis for combining EEG and EMG signals to establish a BCI-based method for lower limb exercise rehabilitation.
Edge devices rely extensively on machinelearning for intelligent inferences and pattern matching. However, edge devices use a multitude of sensing modalities and are exposed to wide ranging contexts. It is difficult ...
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ISBN:
(纸本)9781450358378
Edge devices rely extensively on machinelearning for intelligent inferences and pattern matching. However, edge devices use a multitude of sensing modalities and are exposed to wide ranging contexts. It is difficult to develop separate machinelearning models for each scenario as manual labeling is not scalable. To reduce the amount of labeled data and to speed up the training process, we propose to transfer knowledge between edge devices by using unlabeled data. Our approach, called RecycleML, uses cross modal transfer to accelerate the learning of edge devices across different sensing modalities. Using human activity recognition as a case study, over our collected CMActivity dataset, we observe that RecycleML reduces the amount of required labeled data by at least 90% and speeds up the training process by up to 50 times in comparison to training the edge device from scratch.
A new framework of data assessment and prioritization for real-time prediction of spatial information is presented. In next generation mobile networks, the real-time prediction of spatial information will be a promisi...
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The proceedings contain 147 papers. The topics discussed include: estimation of the common oscillation for phase locked matrix factorization;a general algorithm for calculating force histograms using vector data;on th...
ISBN:
(纸本)9789898425980
The proceedings contain 147 papers. The topics discussed include: estimation of the common oscillation for phase locked matrix factorization;a general algorithm for calculating force histograms using vector data;on the crossover operator for GA-based optimizers in sequential projection pursuit;a dynamic wrapper method for feature discretization and selection;generative embeddings based on rician mixtures - application to kernel-based discriminative classification of magnetic resonance images;clustering complex multimedia objects using an ensemble approach;the stepwise response refinement screener (SRRS) and its applications to analysis of factorial experiments;handling imprecise labels in feature selection with graph Laplacian;the interplay of statistical and structural patternrecognition from a machinelearning perspective;and context sensitive information: model validation by information theory.
The proceedings contain 147 papers. The topics discussed include: estimation of the common oscillation for phase locked matrix factorization;a general algorithm for calculating force histograms using vector data;on th...
ISBN:
(纸本)9789898425980
The proceedings contain 147 papers. The topics discussed include: estimation of the common oscillation for phase locked matrix factorization;a general algorithm for calculating force histograms using vector data;on the crossover operator for GA-based optimizers in sequential projection pursuit;a dynamic wrapper method for feature discretization and selection;generative embeddings based on rician mixtures - application to kernel-based discriminative classification of magnetic resonance images;clustering complex multimedia objects using an ensemble approach;the stepwise response refinement screener (SRRS) and its applications to analysis of factorial experiments;handling imprecise labels in feature selection with graph Laplacian;the interplay of statistical and structural patternrecognition from a machinelearning perspective;and context sensitive information: model validation by information theory.
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