Most of the machine learning algorithms are parametric. What do we mean by parametric? Let's say if we are trying to model an linear regression model with one dependent variable and one independent variable. The best fit we are looking is the line equations with optimized parameters. The parameters could be the intercept and coefficient. For any classification algorithm, we will try to get a boundary.

Word embeddings are increasingly attracting the attention of researchers dealing with semantic similarity and analogy tasks. However, finding the optimal hyper-parameters remains an important challenge due to the resulting impact on the revealed analogies mainly for domain-specific corpora. While analogies are highly used for hypotheses synthesis, it is crucial to optimise word embedding hyper-parameters for precise hypothesis synthesis. Therefore, we propose, in this paper, a methodological approach for tuning word embedding hyper-parameters by using the stability of k-nearest neighbors of word vectors within scientific corpora and more specifically Computer Science corpora with Machine learning adopted as a case study. This approach is tested on a dataset created from NIPS (Conference on Neural Information Processing Systems) publications, and evaluated with a curated ACM hierarchy and Wikipedia Machine Learning outline as the gold standard. Our quantitative and qualitative analysis indicate that our approach not only reliably captures interesting patterns like "unsupervised_learning is to kmeans as supervised_learning is to knn", but also captures the analogical hierarchy structure of Machine Learning and consistently outperforms the \(61\%\) sate-of-the-art embeddings on syntactic accuracy with \(68\%\).

In this paper, we present our method of using fixed-size ordinally forgetting encoding (FOFE) to solve the word sense disambiguation (WSD) problem. FOFE enables us to encode variable-length sequence of words into a theoretically unique fixed-size representation that can be fed into a feed forward neural network (FFNN), while keeping the positional information between words. In our method, a FOFE-based FFNN is used to train a pseudo language model over unlabelled corpus, then the pre-trained language model is capable of abstracting the surrounding context of polyseme instances in labelled corpus into context embeddings. Next, we take advantage of these context embeddings towards WSD classification. We conducted experiments on several WSD data sets, which demonstrates that our proposed method can achieve comparable performance to that of the state-of-the-art approach at the expense of much lower computational cost.

Gatherings of thousands to millions of people occur frequently foran enormous variety of events, and automated counting of these high density crowds is used for safety, management, andmeasuring significance of these events. In this work, we show that the regularly accepted labeling scheme of crowd density maps for training deep neural networks is less effective than our alternative inverse k-nearest neighbor (ikNN) maps, even when used directly in existing state-ofthe-art networkstructures. We also provide a new network architecture MUD-ikNN, which uses multi-scale upsampling via transposed convolutions to take full advantage of the provided ikNN labeling. This upsampling combined with the ikNN maps further outperforms the existing state-of-the-art methods. The full label comparison emphasizes the importance ofthe labeling scheme, with the ikNN labeling being particularly effective. We demonstrate the accuracy of our MUD-ikNN network and the ikNN labeling scheme on a variety of datasets.

Machine learning is in the ascendancy. Particularly when it comes to pattern recognition, machine learning is the method of choice. Tangible examples of its applications include fraud detection, image recognition, predictive maintenance, and train delay prediction systems. In day-to-day machine learning (ML) and the quest to deploy the knowledge gained, we typically encounter these three main problems (but not the only ones). Data Quality – Data from multiple sources across multiple time frames can be difficult to collate into clean and coherent data sets that will yield the maximum benefit from machine learning.

About 1.2 percent of people in the U.S. -- and 3.4 million worldwide -- have active epilepsy, and roughly one in 26 people will develop it in their lifetime. Not all suffer seizures the same -- and for a third of patients, no medical treatment options exist. As for the remaining two thirds, the available treatments don't always behave predictably, owing to the condition's individualized nature. Lack of measurement is a long-standing barrier to better outcomes. Studies show that one common source of data -- written diaries -- tends to be only 50 percent accurate.

Clustering is a widely used unsupervised learning method. The grouping is such that points in a cluster are similar to each other, and less similar to points in other clusters. Thus, it is up to the algorithm to find patterns in the data and group it for us and, depending on the algorithm used, we may end up with different clusters. There are 2 broad approaches for clustering: 1. Compactness -- Points that lie close to each other fall in the same cluster and are compact around the cluster center. The closeness can be measured by the distance between the observations.

MapReduce and its variants have significantly simplified and accelerated the process of developing parallel programs. However, most MapReduce implementations focus on data-intensive tasks while many real-world tasks are compute intensive and their data can fit distributedly into the memory. For these tasks, the speed of MapReduce programs can be much slower than those hand-optimized ones. We present Blaze, a C++ library that makes it easy to develop high performance parallel programs for such compute intensive tasks. At the core of Blaze is a highly-optimized in-memory MapReduce function, which has three main improvements over conventional MapReduce implementations: eager reduction, fast serialization, and special treatment for a small fixed key range. We also offer additional conveniences that make developing parallel programs similar to developing serial programs. These improvements make Blaze an easy-to-use cluster computing library that approaches the speed of hand-optimized parallel code. We apply Blaze to some common data mining tasks, including word frequency count, PageRank, k-means, expectation maximization (Gaussian mixture model), and k-nearest neighbors. Blaze outperforms Apache Spark by more than 10 times on average for these tasks, and the speed of Blaze scales almost linearly with the number of nodes. In addition, Blaze uses only the MapReduce function and 3 utility functions in its implementation while Spark uses almost 30 different parallel primitives in its official implementation.

Speech Acts (SAs) are one of the important areas of pragmatics, which give us a better understanding of the state of mind of the people and convey an intended language function. Knowledge of the SA of a text can be helpful in analyzing that text in natural language processing applications. This study presents a dictionary-based statistical technique for Persian SA recognition. The proposed technique classifies a text into seven classes of SA based on four criteria: lexical, syntactic, semantic, and surface features. WordNet as the tool for extracting synonym and enriching features dictionary is utilized. To evaluate the proposed technique, we utilized four classification methods including Random Forest (RF), Support Vector Machine (SVM), Naive Bayes (NB), and K-Nearest Neighbors (KNN). The experimental results demonstrate that the proposed method using RF and SVM as the best classifiers achieved a state-of-the-art performance with an accuracy of 0.95 for classification of Persian SAs. Our original vision of this work is introducing an application of SA recognition on social media content, especially the common SA in rumors. Therefore, the proposed system utilized to determine the common SAs in rumors. The results showed that Persian rumors are often expressed in three SA classes including narrative, question, and threat, and in some cases with the request SA.

Learn to use Python, the ideal programming language for Machine Learning, with this comprehensive course from Simpliv. Become a complete Machine Learning and Python pro. Our experts will show you how to use your knowledge of Python to learn to use it for Machine Learning. All you need is basic knowledge of Python. Our course will take it up from there and make you an expert.