Analyzing Support Vector Machine Overfitting on Microarray Data

Han, Henry

doi:10.1007/978-3-319-09330-7_19

Cited by 7 publications

(4 citation statements)

References 15 publications

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“…This, however, need not be the case, and other methods, e.g., support vector machines (Vapnik, 2000 ), could be employed for this purpose there. It is worth mentioning, however, that support vector machines might be prone to overfitting (Han, 2014 ) and their training often involves iterative procedures such as sequential quadratic minimization (Platt, 1999 ).…”

Section: Non-iterative Ai Knowledge Transfer Frameworkmentioning

confidence: 99%

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Knowledge Transfer Between Artificial Intelligence Systems

et al. 2018

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We consider the fundamental question: how a legacy “student” Artificial Intelligent (AI) system could learn from a legacy “teacher” AI system or a human expert without re-training and, most importantly, without requiring significant computational resources. Here “learning” is broadly understood as an ability of one system to mimic responses of the other to an incoming stimulation and vice-versa. We call such learning an Artificial Intelligence knowledge transfer. We show that if internal variables of the “student” Artificial Intelligent system have the structure of an n-dimensional topological vector space and n is sufficiently high then, with probability close to one, the required knowledge transfer can be implemented by simple cascades of linear functionals. In particular, for n sufficiently large, with probability close to one, the “student” system can successfully and non-iteratively learn k ≪ n new examples from the “teacher” (or correct the same number of mistakes) at the cost of two additional inner products. The concept is illustrated with an example of knowledge transfer from one pre-trained convolutional neural network to another.

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Section: Non-iterative Ai Knowledge Transfer Frameworkmentioning

confidence: 99%

“…If, for an x generated by an input to AIs, any of i(x) ≥ 0 then report x accordingly (swap labels, report as an error etc.) [22] and their training often involves iterative procedures such as e.g. sequential quadratic minimization [23].…”

Section: Remark 3 Clustering Atmentioning

confidence: 99%

Knowledge Transfer Between Artificial Intelligence Systems

et al. 2018

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“…The calibration and validation results showed that GA-GMN model has better capability to simulate daily discharge as compared to the GMN and SVM models. SVM performed the worst possibly due to its over-fitting features (Han, 2014). Hence, the GA-GMN model has been chosen to predict the future flow characteristics of the selected river.…”

Section: Resultsmentioning

confidence: 99%

River flow prediction with memory-based artificial neural networks: a case study of the Dholai river basin

Debbarma

Choudhury

2020

IJAIP

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Prediction of hydrologic time series has been one of the most challenging tasks in water resources management due to the non-availability of adequate data. Recently, applications of artificial neural networks (ANNs) have proved quite successful in such situation in various fields. This paper demonstrates the use of memory-based ANNs to predict daily river flows. Two different networks, namely the gamma memory neural network (GMN) and genetic algorithm-gamma memory neural network (GA-GMN) have been chosen. The best network topologies for both the ANN models are achieved with Tanh transfer function and Levenberg-Marquardt learning rule after calibrations with multiple combinations of network parameters. The selected ANN models are then used to predict the daily mean flows of Dholai (Rukmi) river in Assam, India, a sub-basin of the Barak river basin. A comparative study of both networks indicates that the GA-GMN model performed better than the GMN model. The GA-GMN model gave better results for both training and testing dataset with minimum training MSE as 0.018 and minimum testing MSE as 22.97. Hence GA-GMN model is selected as an effective tool for predicting flow features of the Dholai river.

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“…We compare the proposed tensor-based classifier OCSTM with vector-based classifier OCSVM on BREAST-CANCER dataset. Similar to the idea of [32,33], to verify the effectiveness dealt with overfitting problem, we use these two evaluation indexes: sensitivity and specificity, where:…”

Section: Experiments On Overfitting Problemmentioning

confidence: 99%

One-Class Support Tensor Machine

Chen

Wang

Zhong

2016

Knowledge-Based Systems

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In fault diagnosis, face recognition, network anomaly detection, text classification and many other fields, we often encounter one-class classification problems. The traditional vector-based one-class classification algorithms represented by one-class Support Vector Machine (OCSVM) have limitations when tensor is considered as input data. This work addresses one-class classification problem with tensor-based maximal margin classification paradigm. To this end, we formulate the One-Class Support Tensor Machine (OCSTM), which separates most samples of interested class from the origin in the tensor space, with maximal margin. The benefits of the proposed algorithm are twofold. First, the use of direct tensor representation helps to retain the data topology more efficiently. The second benefit is that tensor representation can greatly reduce the number of parameters. It helps overcome the overfitting problem caused mostly by vector-based algorithms and especially suits for high dimensional and small sample size problem. To solve the corresponding optimization problem in OCSTM, the alternating projection method is implemented, for it is simplified by solving a typical OCSVM optimization problem at each iteration. The efficiency of the proposed method is illustrated on both vector and tensor datasets. The experimental results indicate the validity of the new method.

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Analyzing Support Vector Machine Overfitting on Microarray Data

Cited by 7 publications

References 15 publications

Knowledge Transfer Between Artificial Intelligence Systems

Knowledge Transfer Between Artificial Intelligence Systems

River flow prediction with memory-based artificial neural networks: a case study of the Dholai river basin

One-Class Support Tensor Machine

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