Basics of qualitative research: Grounded theory procedures and techniques

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

Network embedding aims to learn low-dimensional representations of nodes in a network, while the network structure and inherent properties are preserved. It has attracted tremendous attention recently due to significant progress in downstream network learning tasks, such as node classification, link prediction, and visualization. However, most existing network embedding methods suffer from the expensive computations due to the large volume of networks. In this paper, we propose a $10\times \sim 100\times$ faster network embedding method, called Progle, by elegantly utilizing the sparsity property of online networks and spectral analysis. In Progle, we first construct a \textit{sparse} proximity matrix and train the network embedding efficiently via sparse matrix decomposition. Then we introduce a network propagation pattern via spectral analysis to incorporate local and global structure information into the embedding. Besides, this model can be generalized to integrate network information into other insufficiently trained embeddings at speed. Benefiting from sparse spectral network embedding, our experiment on four different datasets shows that Progle outperforms or is comparable to state-of-the-art unsupervised comparison approaches---DeepWalk, LINE, node2vec, GraRep, and HOPE, regarding accuracy, while is $10\times$ faster than the fastest word2vec-based method. Finally, we validate the scalability of Progle both in real large-scale networks and multiple scales of synthetic networks.

Spectral Network Embedding: A Fast and Scalable Method via Sparsity.

We present a manually-labeled Author Name Disambiguation(AND) Dataset called WhoisWho, which consists of 399,255 documents and 45,187 distinct authors with 421 ambiguous author names To label such a great amount of AND data of high accuracy, we propose a novel annotation framework where the human and computer collaborate efficiently and precisely Within the framework, we also propose an inductive disambiguation model to classify whether two documents belong to the same author We evaluate the proposed method and other state-of-the-art disambiguation methods on WhoisWho The experiment results show that: (1) Our model outperforms other disambiguation algorithms on this challenging benchmark (2) The AND problem still remains largely unsolved and requires more in-depth research We believe that such a large-scale benchmark would bring great value for the author name disambiguation task We also conduct several experiments to prove our annotation framework could assist annotators to make accurate results efficiently and eliminate wrong label problems made by human annotators effectively

A framework for constructing a huge name disambiguation dataset: algorithms, visualization and human collaboration.

In a directory ontology, only concepts and hypernym/hyponym relationships between them are defined. Directory ontologies are widely used in many real-world applications such as product catalogues and Web directories. The widely used heterogeneous directories also bring a big challenge for integration of them. Previously, little attentions have been paid attention to the problem in the research community. In this paper, we propose a path similarity based approach for directory ontology matching. We define the concept's local label and the concept's path label. Next, we propose a path similarity method, which combines the local label and path label information. Then, a top-down similarity flooding is utilized to improve the matching result. Our experimental results on the music genre ontologies show that the proposed method can achieve a precision of 78% and a recall of 66%, significantly outperforming the baseline method.

Path Similarity Based Directory Ontology Matching

Inferring Social Ties.

Many measuring semantic similarity, with different methods, are applied in Natural Language Processing, knowledge acquisition and information retrieval. Recently, some authors have extended some of the existing methodologies to support multiple ontologies to improve the correlation values. In this paper, a feature-based method with heuristic function is proposed to deal with multi-ontology. By comparing the correlation values attained in this method with those of Pedersen’s biomedical benchmark, a higher accuracy is achieved.

Linked Data and Knowledge Graph: 7th Chinese Semantic Web Symposium and 2nd Chinese Web Science Conference, CSWS 2013 Shanghai, China, August 12-16, 2013 Revised Selected Papers

Jie Tang

Papers

Spectral Network Embedding: A Fast and Scalable Method via Sparsity.

A framework for constructing a huge name disambiguation dataset: algorithms, visualization and human collaboration.

Path Similarity Based Directory Ontology Matching

Inferring Social Ties.

Linked Data and Knowledge Graph: 7th Chinese Semantic Web Symposium and 2nd Chinese Web Science Conference, CSWS 2013 Shanghai, China, August 12-16, 2013 Revised Selected Papers