Svm incremental learning, adaptation and optimization

Diehl, Christopher P.; Cauwenberghs, Gert

doi:10.1109/ijcnn.2003.1223991

Cited by 189 publications

(115 citation statements)

References 2 publications

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“…In contrast with these existing works, our algorithm implements incremental learning on kd-tree based KNN. The algorithm can bring a lower incremental time consumption in incremental phase than some other algorithms like [15] and [17]. In addition, after borrowing idea from [6], the adaptation of a hybrid incremental learning makes our algorithm in a high level of all the incremental learning algorithms.…”

Section: Background and Related Workmentioning

confidence: 99%

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IKNN-SVM: A Hybrid Incremental Algorithm for Image Classification

Che¹,

Ding²,

Wang³

et al. 2016

Proceedings of the 2016 2nd International Conference on Artificial Intelligence and Industrial Engineering (AIIE 2016)

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Abstract-Incremental learning is a machine learning paradigm that the training dataset grows dynamically at runtime and the prediction accuracy can be improved incrementally. Since reallife datasets are usually open-ended and dynamic, incremental learning is important to many image classification applications. In existing incremental algorithms, KNN (K Nearest Neighbors) has the advantage of dealing with the hugely and incrementally multiclass nature of visual object recognition. However, it provides good prediction only when the query image is similar to one of the images in the dataset, which results in low accuracy in many cases. In this paper, we propose a hybrid incremental learning algorithm named IKNN-SVM which can improve the recognition accuracy while reserving the advantages of KNN, i.e., quickly learning from the increasing number of data. To achieve this goal, our incremental algorithm combines SVM (Support Vector Machine) with high recognition accuracy and KNN together. And to promote the incremental learning efficiency, IKNN-SVM introduce kd-tree to implement fast searching. The experimental result based on the open dataset Caltech256 shows that the recognition accuracy of our method improves by 12%-26% compared with incremental KNN but with short time consumption.

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Section: Background and Related Workmentioning

confidence: 99%

“…Therefore, incremental learning methods has been widely used for image classification. Support vector based incremental learning method such as [15], which uses Karush-Kuhn-Tucker (KKT) conditions, adiabatic increments and bookkeeping method. In [16], the authors have implemented an incremental SVM based on linear kernel.…”

Section: Background and Related Workmentioning

confidence: 99%

IKNN-SVM: A Hybrid Incremental Algorithm for Image Classification

Che¹,

Ding²,

Wang³

et al. 2016

Proceedings of the 2016 2nd International Conference on Artificial Intelligence and Industrial Engineering (AIIE 2016)

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“…Thus, we define a neighborhood set centered around C t * as C t * ε , εC t * , where ε > 1. We update both the k-CV training set and validation set and, accordingly, the f t+1 1,...,k by using a regularization parameter optimization technique (Diehl & Cauwenberghs, 2003) for every value of C ∈ C t * ε , εC t * . We thus identify the best regularization parameter configuration C t+1 * .…”

Section: Incremental Model Selection For Svmmentioning

confidence: 99%

Adaptive SVM for Data Stream Classification

Abdulkarim¹

2017

SACJ

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In this paper, we address the problem of learning an adaptive classifier for the classification of continuous streams of data. We present a solution based on incremental extensions of the Support Vector Machine (SVM) learning paradigm that updates an existing SVM whenever new training data are acquired. To ensure that the SVM effectiveness is guaranteed while exploiting the newly gathered data, we introduce an on-line model selection approach in the incremental learning process. We evaluated the proposed method on real world applications including on-line spam email filtering and human action classification from videos. Experimental results show the effectiveness and the potential of the proposed approach.

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“…For this it is necessary either to design a mechanism of online learning or perform an adaptation of the learnt SVM classifier to new environmental acoustics similar to MAP or MLLR adaptation for GMM. Some works on the former approach can be found in the literature [DC03] while the latter is still an open question.…”

Section: Acoustic Event Detection In Real Environmentsmentioning

confidence: 99%

Acoustic Event Detection and Classification

Temko

Nadeu

Macho

et al. 2009

Computers in the Human Interaction Loop

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The human activity that takes place in meeting-rooms or class-rooms is reflected in a rich variety of acoustic events, either produced by the human body or by objects handled by humans, so the determination of both the identity of sounds and their position in time may help to detect and describe that human activity. Additionally, detection of sounds other than speech may be useful to enhance the robustness of speech technologies like automatic speech recognition.Automatic detection and classification of acoustic events is the objective of this thesis work. It aims at processing the acoustic signals collected by distant microphones in meeting-room or classroom environments to convert them into symbolic descriptions corresponding to a listener's perception of the different sound events that are present in the signals and their sources.First of all, the task of acoustic event classification is faced using Support Vector Machine (SVM) classifiers, which are motivated by the scarcity of training data. A confusion-matrix-based variable-feature-set clustering scheme is developed for the multiclass recognition problem, and tested on the gathered database. With it, a higher classification rate than the GMM-based technique is obtained, arriving to a large relative average error reduction with respect to the best result from the conventional binary tree scheme. Moreover, several ways to extend SVMs to sequence processing are compared, in an attempt to avoid the drawback of SVMs when dealing with audio data, i.e. their restriction to work with fixed-length vectors, observing that the dynamic time warping kernels work well for sounds that show a temporal structure. Furthermore, concepts and tools from the fuzzy theory are used to investigate, first, the importance of and degree of interaction among features, and second, ways to fuse the outputs of several classification systems. The developed AEC systems are Two kinds of databases are used: two databases of isolated acoustic events, and a database of interactive seminars containing a significant number of acoustic events of interest. Our developed systems, which consist of SVM-based classification within a sliding window plus post-processing, were the only submissions not using HMMs, and each of them obtained competitive results in the corresponding evaluation.vi Speech activity detection was also pursued in this thesis since, in fact, it is a -especially important -particular case of acoustic event detection. An enhanced SVM training approach for the speech activity detection task is developed, mainly to cope with the problem of dataset reduction.The resulting SVM-based system is tested with several NIST Rich Transcription (RT) evaluation datasets, and it shows better scores than our GMM-based system, which ranked among the best systems in the RT06 evaluation.Finally, it is worth mentioning a few side outcomes from this thesis work. As it has been carried out in the framework of the CHIL EU project, the author has been responsible for the organization of the above m...

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Svm incremental learning, adaptation and optimization

Cited by 189 publications

References 2 publications

IKNN-SVM: A Hybrid Incremental Algorithm for Image Classification

IKNN-SVM: A Hybrid Incremental Algorithm for Image Classification

Adaptive SVM for Data Stream Classification

Acoustic Event Detection and Classification

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