A machine learning algorithm for image classification exhibits spatial bias if permuting the order of image pixels significantly alters its classification accuracy. In this paper, we explore the spatial bias of a number of different distance metrics for k-nearest-neighbor image classification. One distance metric is inspired by the convolutional kernels employed in convolutional neural networks. The other metrics are based on BRIEF descriptors, which generate bit vectors corresponding to images based on comparisons of pixel intensity values. We found that the convolutional distance metric exhibited a strong positive spatial bias, as did one of the BRIEF descriptors. Another BRIEF descriptor exhibited a negative spatial bias, and the remainder exhibited little or no spatial bias. These results lay a foundation for future work that would involve larger numbers of convolutional iterations, potentially synergized with BRIEF-style image preprocessing.

Researchers seek to identify biological markers which accurately differentiate cancer subtypes and their severity from normal controls. One such biomarker, DNA methylation, has recently become more prevalent in genetic research studies in oncology. This paper proposes to apply these findings in a study of the diagnostic accuracy of DNA methylation signatures for classifying metastasis samples. Very high classification performance measures were obtained from differentially methylated positions and regions, as well as from selected gene signatures. Perfect accuracy was achieved with the top 5 feature-selected genes using three similar cases and the K-nearest neighbor classfier. This work contributes to the path toward the identification of biological signatures for oncology samples using case-based reasoning.

kNN is a very effective Instance based learning method, and it is easy to implement. Due to heterogeneous nature of data, noises from different possible sources are also widespread in nature especially in case of large-scale databases. For noise elimination and effect of pseudo neighbours, in this paper, we propose a new learning algorithm which performs the task of anomaly detection and removal of pseudo neighbours from the dataset so as to provide comparative better results. This algorithm also tries to minimize effect of those neighbours which are distant. A concept of certainty measure is also introduced for experimental results. The advantage of using concept of mutual neighbours and distance-weighted voting is that, dataset will be refined after removal of anomaly and weightage concept compels to take into account more consideration of those neighbours, which are closer. Consequently, finally the performance of proposed algorithm is calculated.

Machine learning (ML) applications have been thriving recently, largely attributed to the increasing availability of data. However, inconsistency and incomplete information are ubiquitous in real-world datasets, and their impact on ML applications remains elusive. In this paper, we present a formal study of this impact by extending the notion of Certain Answers for Codd tables, which has been explored by the database research community for decades, into the field of machine learning. Specifically, we focus on classification problems and propose the notion of "Certain Predictions" (CP) -- a test data example can be certainly predicted (CP'ed) if all possible classifiers trained on top of all possible worlds induced by the incompleteness of data would yield the same prediction. We study two fundamental CP queries: (Q1) checking query that determines whether a data example can be CP'ed; and (Q2) counting query that computes the number of classifiers that support a particular prediction (i.e., label). Given that general solutions to CP queries are, not surprisingly, hard without assumption over the type of classifier, we further present a case study in the context of nearest neighbor (NN) classifiers, where efficient solutions to CP queries can be developed -- we show that it is possible to answer both queries in linear or polynomial time over exponentially many possible worlds. We demonstrate one example use case of CP in the important application of "data cleaning for machine learning (DC for ML)." We show that our proposed CPClean approach built based on CP can often significantly outperform existing techniques in terms of classification accuracy with mild manual cleaning effort.

You guys are mostly familiar with the Trending word Machine Learning . Some of you also know the types of Machine Learning . So you must be wondering what value you will get in the article . See, We all know generally, There are 3 types of Machine Learning: Supervised, Unsupervised, reinforcement Learning . Some of us have also read about semi supervised learning as hybrid of supervised and unsupervised learning .

Machine learning involves the use of machine learning algorithms and models. For beginners, this is very confusing as often "machine learning algorithm" is used interchangeably with "machine learning model." Are they the same thing or something different? As a developer, your intuition with "algorithms" like sort algorithms and search algorithms will help to clear up this confusion. In this post, you will discover the difference between machine learning "algorithms" and "models."

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We introduce kNN-LMs, which extend a pre-trained neural language model (LM) by linearly interpolating it with a k-nearest neighbors (kNN) model. The nearest neighbors are computed according to distance in the pre-trained LM embedding space, and can be drawn from any text collection, including the original LM training data. Applying this augmentation to a strong WIKITEXT-103 LM, with neighbors drawn from the original training set, our kNN-LM achieves a new state-of-the-art perplexity of 15.79 – a 2.9 point improvement with no additional training. We also show that this approach has implications for efficiently scaling up to larger training sets and allows for effective domain adaptation, by simply varying the nearest neighbor datastore, again without further training. Qualitatively, the model is particularly helpful in predicting rare patterns, such as factual knowledge.

The power grid frequency is the central observable in power system control, as it measures the balance of electrical supply and demand. A reliable frequency forecast can facilitate rapid control actions and may thus greatly improve power system stability. Here, we develop a weighted-nearest-neighbor (WNN) predictor to investigate how predictable the frequency trajectories are. Our forecasts for up to one hour are more precise than averaged daily profiles and could increase the efficiency of frequency control actions. Furthermore, we gain an increased understanding of the specific properties of different synchronous areas by interpreting the optimal prediction parameters (number of nearest neighbors, the prediction horizon, etc.) in terms of the physical system. Finally, prediction errors indicate the occurrence of exceptional external perturbations. Overall, we provide a diagnostics tool and an accurate predictor of the power grid frequency time series, allowing better understanding of the underlying dynamics.

Local learning methods are a popular class of machine learning algorithms. The basic idea for the entire cadre is to choose some non-local model family, to train many of them on small sections of neighboring data, and then to `stitch' the resulting models together in some way. Due to the limits of constraining a training dataset to a small neighborhood, research on locally-learned models has largely been restricted to simple model families. Also, since simple model families have no complex structure by design, this has limited use of the individual local models to predictive tasks. We hypothesize that, using a sufficiently complex local model family, various properties of the individual local models, such as their learned parameters, can be used as features for further learning. This dissertation improves upon the current state of research and works toward establishing this hypothesis by investigating algorithms for localization of more complex model families and by studying their applications beyond predictions as a feature extraction mechanism. We summarize this generic technique of using local models as a feature extraction step with the term ``local model feature transformations.'' In this document, we extend the local modeling paradigm to Gaussian processes, orthogonal quadric models and word embedding models, and extend the existing theory for localized linear classifiers. We then demonstrate applications of local model feature transformations to epileptic event classification from EEG readings, activity monitoring via chest accelerometry, 3D surface reconstruction, 3D point cloud segmentation, handwritten digit classification and event detection from Twitter feeds.