On Noise Effect in Semi-supervised Learning
نویسندگان
چکیده
The article deals with the problem of noise effect on semi-supervised learning. goal this is to analyze impact accuracy binary classification models created using three learning algorithms, namely Simple Recycled Selection, Incrementally Reinforced and Hybrid Algorithm, Support Vector Machines build a base classifier. Different algorithms compute similarity matrices, Radial Bias Function, Cosine Similarity, K-Nearest Neighbours were analyzed understand their model accuracy. For benchmarking purposes, datasets from UCI repository used. To test effect, different amounts artificially generated randomly-labeled samples introduced into dataset strategies (labeled, unlabeled, mixed) compared baseline classifier trained original reduced-size dataset. results show that introduction random labeled decreases accuracy, while moderate amount in unmarked can have positive
منابع مشابه
Noise-Robust Semi-Supervised Learning by Large-Scale Sparse Coding
This paper presents a large-scale sparse coding algorithm to deal with the challenging problem of noiserobust semi-supervised learning over very large data with only few noisy initial labels. By giving an L1-norm formulation of Laplacian regularization directly based upon the manifold structure of the data, we transform noise-robust semi-supervised learning into a generalized sparse coding prob...
متن کاملCoupled Semi-Supervised Learning
This thesis argues that successful semi-supervised learning is improved by learning many functions at once in a coupled manner. Given knowledge about constraints between functions to be learned (e.g., f1(x) → ¬f2(x)), forcing the models that are learned to obey these constraints can yield a more constrained, and therefore easier, set of learning problems. We apply these ideas to bootstrap learn...
متن کاملSemi-supervised Learning
Semi-supervised learning uses both labeled and unlabeled data to perform an otherwise supervised learning or unsupervised learning task. In the former case, there is a distinction between inductive semi-supervised learning and transductive learning. In inductive semi-supervised learning, the learner has both labeled training data {(xi, yi)}i=1 iid ∼ p(x, y) and unlabeled training data {xi} i=l+...
متن کاملSemi-Supervised Learning
For many classification problems, unlabeled training data are inexpensive and readily available, whereas labeling training data imposes costs. Semi-supervised classification algorithms aim at utilizing information contained in unlabeled data in addition to the (few) labeled data. Semi-supervised (for an example, see Seeger, 2001) has a long tradition in statistics (Cooper & Freeman, 1970); much...
متن کاملSemi-Supervised Structure Learning
Discriminative learning framework is one of the very successful fields of machine learning. The methods of this paradigm, such as Boosting, and Support Vector Machines have significantly advanced the state-of-the-art for classification by improving the accuracy and by increasing the applicability of machine learning methods. Recently there has been growing interest to generalize discrimative le...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Elektronìka ta sistemi upravlìnnâ
سال: 2022
ISSN: ['1990-5548']
DOI: https://doi.org/10.18372/1990-5548.71.16816