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On computable learning of continuous features [article]

Nathanael Ackerman and Julian Asilis and Jieqi Di and Cameron Freer and Jean-Baptiste Tristan
2021 arXiv   pre-print
We introduce definitions of computable PAC learning for binary classification over computable metric spaces.  ...  We also give a presentation of a hypothesis class that does not admit any proper computable PAC learner with computable sample function, despite the underlying class being PAC learnable.  ...  Acknowledgements The authors would like to thank Caleb Miller for valuable discussion on the topic, particularly in helping refine the notion of computable PAC learning and in describing the computable  ... 
arXiv:2111.14630v1 fatcat:bd4rfuldhfat5p4rxtqr7keply

A Review of Intrusion Detection Technique using Various Technique of Machine Learning and Feature Optimization Technique

Sunita Patel, Jyoti Sondhi
2014 International Journal of Computer Applications  
Machine learning technique is collection of all learning algorithm such as classification, clustering and regression. for the improvement of machine learning technique used feature optimization technique  ...  In this paper presents review of intrusion detection technique using machine learning and feature optimization process.  ...  The computer security community has developed a variety of intrusion detection systems to prevent attacks on computer systems.  ... 
doi:10.5120/16286-6185 fatcat:ndkhu3ztwfhgrpxbtzrkqe6rsu

Gerçek Ortamlarda Artımlı Öğrenme ile Gerçek Zamanlı İşitsel Sahne Analizi

Barış BAYRAM, Gökhan İNCE
2020 European Journal of Science and Technology  
The continual learning is employed via a time-series algorithm, Hidden Markov Model (HMM), on these feature sets from acoustic signals stemming from the sources.  ...  We verified the effectiveness of the proposed system capable of continual learning, AER and UED in terms of False-Positive Rates, True-Positive Rates, recognition accuracy and computational time to meet  ...  In general, most existing approaches suffer from a high computational cost; so there is apparently a lack of studies on continual learning in realtime.  ... 
doi:10.31590/ejosat.779710 fatcat:5i2rwdh23ndgzkr7dy4redk7my

Continual Learning for Food Recognition Using Class Incremental Extreme and Online Clustering Method: Self-Organizing Incremental Neural Network

Rashida Bansiwala, Pramod Gosavi, Rahul Gaikwad
2021 International journal of innovations in engineering and science  
This paper proposes a new open-ended continual learning framework by employing transfer learning on deep models for feature extraction, Relief F for feature selection, and a novel adaptive reduced class  ...  However, most of the existing dietary assessment methods rely on memory.  ...  of open-ended continual learning.  ... 
doi:10.46335/ijies.2021.6.10.7 fatcat:dpcq2ibswncbjlwqipassf2nhy

RSAC: Regularized Subspace Approximation Classifier for Lightweight Continuous Learning [article]

Chih-Hsing Ho, Shang-Ho Tsai
2020 arXiv   pre-print
Continuous learning seeks to perform the learning on the data that arrives from time to time.  ...  Extensive experiments show that RSAC is more efficient than prior continuous learning works and outperforms these works on various experimental settings.  ...  Moreover, the learning of θ requires the backpropagation procedure, which is computation expensive and relies on great amount of computing resources.  ... 
arXiv:2007.01480v1 fatcat:rqzyashyangtloa7uyyn472md4

Learning Object-Centered Autotelic Behaviors with Graph Neural Networks [article]

Ahmed Akakzia, Olivier Sigaud
2022 arXiv   pre-print
In this paper, we propose to investigate the impact of these representations on the learning capabilities of autotelic agents.  ...  Although humans live in an open-ended world and endlessly face new challenges, they do not have to learn from scratch each time they face the next one.  ...  At test time, the per-class performance of the agent is computed on 24 goals of each evaluation class (264 semantic goals and 120 continuous goals).  ... 
arXiv:2204.05141v1 fatcat:pb44nkgtxzbbrk62ox2cdnoy7m

Continual Learning for Anomaly Detection in Surveillance Videos [article]

Keval Doshi, Yasin Yilmaz
2020 arXiv   pre-print
While current state-of-the-art deep learning approaches perform well on existing public datasets, they fail to work in a continual learning framework due to computational and storage issues.  ...  Our proposed algorithm leverages the feature extraction power of neural network-based models for transfer learning, and the continual learning capability of statistical detection methods.  ...  A key component of computer vision problems is the extraction of meaningful features.  ... 
arXiv:2004.07941v1 fatcat:nga25izgjja2nkxv7wor55xg6i

Learning to predict trajectories of cooperatively navigating agents

Henrik Kretzschmar, Markus Kuderer, Wolfram Burgard
2014 2014 IEEE International Conference on Robotics and Automation (ICRA)  
To compute the feature expectations over the highdimensional continuous distributions, we use Hamiltonian Markov chain Monte Carlo sampling.  ...  Our approach learns the model parameters of this distribution that match, in expectation, the observed behavior in terms of user-defined features.  ...  All authors are with the Department of Computer Science, University of Freiburg, Germany.  ... 
doi:10.1109/icra.2014.6907442 dblp:conf/icra/KretzschmarKB14 fatcat:kicoic5byvecnjhgpsptaspuk4

Per instance algorithm configuration of CMA-ES with limited budget

Nacim Belkhir, Johann Dréo, Pierre Savéant, Marc Schoenauer
2017 Proceedings of the Genetic and Evolutionary Computation Conference on - GECCO '17  
Special care is taken to the computational cost of the features.  ...  setting of CMA-ES with as few as 30 or 50 time the problem dimension additional function evaluations for feature computation.  ...  FEATURES FOR CONTINUOUS DOMAIN In the black-box context, only samples of function values can be used to compute the features 2 .  ... 
doi:10.1145/3071178.3071343 dblp:conf/gecco/BelkhirDSS17 fatcat:2u2bln6drba4xahl2ghbvjjkvu

A Review and Empirical Evaluation of Feature Weighting Methods for a Class of Lazy Learning Algorithms [chapter]

Dietrich Wettschereck, David W. Aha, Takao Mohri
1997 Lazy Learning  
We also found that continuous weighting methods tend to outperform feature selection algorithms for tasks where some features are useful but less important than others.  ...  interacting features, and generally require less training data to learn good settings.  ...  Finally, we thank Patrick Murphy for maintaining the UCI Repository of ML Databases and Robert Detrano for making available the datasets on heart disease diagnoses.  ... 
doi:10.1007/978-94-017-2053-3_11 fatcat:zq6gskezjbcsrjlj7s3vldtd7a

OvA-INN: Continual Learning with Invertible Neural Networks [article]

G. Hocquet, O. Bichler, D. Querlioz
2020 arXiv   pre-print
This way, we are able to outperform state-of-the-art approaches that rely on features learning for the Continual Learning of MNIST and CIFAR-100 datasets.  ...  In the field of Continual Learning, the objective is to learn several tasks one after the other without access to the data from previous tasks.  ...  data; (iii) state-of-the-art results on several tasks of Continual Learning for Computer Vision (CIFAR-100, MNIST) in this setting.  ... 
arXiv:2006.13772v1 fatcat:nwr5ccrtvjerfna4n4dpkr5ecu


Dietrich Wettschereck, David W. Aha, Takao Mohri
2012 Artificial Intelligence Review  
We also found that continuous weighting methods tend to outperform feature selection algorithms for tasks where some features are useful but less important than others.  ...  interacting features, and generally require less training data to learn good settings.  ...  Finally, we thank Patrick Murphy for maintaining the UCI Repository of ML Databases and Robert Detrano for making available the datasets on heart disease diagnoses.  ... 
doi:10.1023/a:1006593614256 fatcat:ie42jwxuivbelblbvy2tzd2ys4

An Emotion-embedded Visual Attention Model for Dimensional Emotion Context Learning

Yuhao Tang, Qirong Mao, Hongjie Jia, Heping Song, Yongzhao Zhan
2019 IEEE Access  
Second, a visual attention model based on the gated recurrent unit (GRU) is employed to learn the context information of the feature sequences from face features.  ...  In this paper, all experiments are carried out on database AVEC 2016 and AVEC 2017. The experimental results validate the efficiency of our method, and competitive results are obtained.  ...  Unfocused learning of facial features by sequence model are not able to fully capture the variations continuously changing from one emotion to another.  ... 
doi:10.1109/access.2019.2911714 fatcat:a2nsjbzelnfyrd227p5x4qfs6i

A Continuous Learning Framework for Activity Recognition Using Deep Hybrid Feature Models

Mahmudul Hasan, Amit K. Roy-Chowdhury
2015 IEEE transactions on multimedia  
Some recent approaches on activity recognition use deep-learning-based hierarchical feature models, but the large size of these networks constrain them from being used in continuous learning scenarios.  ...  Most of the research on human activity recognition has focused on learning a static model, considering that all the training instances are labeled and present in advance, while in streaming videos new  ...  Evaluation of Continuous Learning on Individual Activities F.  ... 
doi:10.1109/tmm.2015.2477242 fatcat:ve55gpwl3zaljcgsxgvyo6jfwy

Combining Gradient Boosting Machines with Collective Inference to Predict Continuous Values [article]

Iman Alodah, Jennifer Neville
2016 arXiv   pre-print
However, much of the work on collective learning and inference has focused on discrete prediction tasks rather than continuous.  ...  Gradient boosting of regression trees is a competitive procedure for learning predictive models of continuous data that fits the data with an additive non-parametric model.  ...  The relational features that we compute are aggregations of continuous class values (e.g., average and median).  ... 
arXiv:1607.00110v1 fatcat:7ly6rnzcdzguplc2bjd4hygf6e
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