Efficient Learning of Pinball TWSVM using Privileged Information and its applications

• URL: http://arxiv.org/abs/2107.06744v1
• Date: Wed, 14 Jul 2021 14:42:07 GMT
• Title: Efficient Learning of Pinball TWSVM using Privileged Information and its applications
• Authors: Reshma Rastogi (nee. Khemchandani) and Aman Pal
• Abstract summary: We propose privileged information based Twin Pinball Support Vector Machine classifier (Pin-TWSVMPI) The proposed Pin-TWSVMPI incorporates privileged information by using correcting function so as to obtain two nonparallel decision hyperplanes. For UCI datasets, we first implement a procedure which extracts privileged information from the features of the dataset which are then further utilized by Pin-TWSVMPI.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In any learning framework, an expert knowledge always plays a crucial role. But, in the field of machine learning, the knowledge offered by an expert is rarely used. Moreover, machine learning algorithms (SVM based) generally use hinge loss function which is sensitive towards the noise. Thus, in order to get the advantage from an expert knowledge and to reduce the sensitivity towards the noise, in this paper, we propose privileged information based Twin Pinball Support Vector Machine classifier (Pin-TWSVMPI) where expert's knowledge is in the form of privileged information. The proposed Pin-TWSVMPI incorporates privileged information by using correcting function so as to obtain two nonparallel decision hyperplanes. Further, in order to make computations more efficient and fast, we use Sequential Minimal Optimization (SMO) technique for obtaining the classifier and have also shown its application for Pedestrian detection and Handwritten digit recognition. Further, for UCI datasets, we first implement a procedure which extracts privileged information from the features of the dataset which are then further utilized by Pin-TWSVMPI that leads to enhancement in classification accuracy with comparatively lesser computational time.

Related papers

• Layer Attack Unlearning: Fast and Accurate Machine Unlearning via Layer Level Attack and Knowledge Distillation [21.587358050012032]
  We propose a fast and novel machine unlearning paradigm at the layer level called layer attack unlearning. In this work, we introduce the Partial-PGD algorithm to locate the samples to forget efficiently. We also use Knowledge Distillation (KD) to reliably learn the decision boundaries from the teacher.
  arXiv  Detail & Related papers  (2023-12-28T04:38:06Z)
• XAL: EXplainable Active Learning Makes Classifiers Better Low-resource Learners [71.8257151788923]
  We propose a novel Explainable Active Learning framework (XAL) for low-resource text classification. XAL encourages classifiers to justify their inferences and delve into unlabeled data for which they cannot provide reasonable explanations. Experiments on six datasets show that XAL achieves consistent improvement over 9 strong baselines.
  arXiv  Detail & Related papers  (2023-10-09T08:07:04Z)
• Computing Rule-Based Explanations of Machine Learning Classifiers using Knowledge Graphs [62.997667081978825]
  We use knowledge graphs as the underlying framework providing the terminology for representing explanations for the operation of a machine learning classifier. In particular, we introduce a novel method for extracting and representing black-box explanations of its operation, in the form of first-order logic rules expressed in the terminology of the knowledge graph.
  arXiv  Detail & Related papers  (2022-02-08T16:21:49Z)
• Knowledge Informed Machine Learning using a Weibull-based Loss Function [0.0]
  knowledge informed machine learning can be enhanced through the integration of external knowledge. A knowledge informed machine learning technique is demonstrated, using the common IMS and PRONOSTIA bearing data sets. A thorough statistical analysis of the Weibull-based loss function is conducted, demonstrating the effectiveness of the method on the PRONOSTIA data set.
  arXiv  Detail & Related papers  (2022-01-04T22:53:14Z)
• A Ternary Bi-Directional LSTM Classification for Brain Activation Pattern Recognition Using fNIRS [0.15229257192293197]
  Functional near-infrared spectroscopy (fNIRS) is a non-invasive, low-cost method used to study the brain's blood flow pattern. The proposed system uses a Bi-Directional LSTM based deep learning architecture for task classification.
  arXiv  Detail & Related papers  (2021-01-14T22:21:15Z)
• Representation Learning for Sequence Data with Deep Autoencoding Predictive Components [96.42805872177067]
  We propose a self-supervised representation learning method for sequence data, based on the intuition that useful representations of sequence data should exhibit a simple structure in the latent space. We encourage this latent structure by maximizing an estimate of predictive information of latent feature sequences, which is the mutual information between past and future windows at each time step. We demonstrate that our method recovers the latent space of noisy dynamical systems, extracts predictive features for forecasting tasks, and improves automatic speech recognition when used to pretrain the encoder on large amounts of unlabeled data.
  arXiv  Detail & Related papers  (2020-10-07T03:34:01Z)
• Federated Doubly Stochastic Kernel Learning for Vertically Partitioned Data [93.76907759950608]
  We propose a doubly kernel learning algorithm for vertically partitioned data. We show that FDSKL is significantly faster than state-of-the-art federated learning methods when dealing with kernels.
  arXiv  Detail & Related papers  (2020-08-14T05:46:56Z)
• Privileged Information Dropout in Reinforcement Learning [56.82218103971113]
  Using privileged information during training can improve the sample efficiency and performance of machine learning systems. In this work, we investigate Privileged Information Dropout (pid) for achieving the latter which can be applied equally to value-based and policy-based reinforcement learning algorithms.
  arXiv  Detail & Related papers  (2020-05-19T05:32:33Z)
• Knowledge Graph semantic enhancement of input data for improving AI [0.0]
  Intelligent systems designed using machine learning algorithms require a large number of labeled data. Background knowledge provides complementary, real world factual information that can augment the limited labeled data to train a machine learning algorithm. The Knowledge Graph (KG) is in vogue as for many practical applications, it is convenient and useful to organize this background knowledge in the form of a graph.
  arXiv  Detail & Related papers  (2020-05-10T17:37:38Z)
• Mining Implicit Entity Preference from User-Item Interaction Data for Knowledge Graph Completion via Adversarial Learning [82.46332224556257]
  We propose a novel adversarial learning approach by leveraging user interaction data for the Knowledge Graph Completion task. Our generator is isolated from user interaction data, and serves to improve the performance of the discriminator. To discover implicit entity preference of users, we design an elaborate collaborative learning algorithms based on graph neural networks.
  arXiv  Detail & Related papers  (2020-03-28T05:47:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.