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1. |
Record Nr. |
UNINA9910706727003321 |
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Titolo |
Mortgage Choice Act of 2017 : report together with minority views (to accompany H.R. 1153) (including cost estimate of the Congressional Budget Office) |
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Pubbl/distr/stampa |
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[Washington, D.C.] : , : [U.S. Government Publishing Office], , [2018] |
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Descrizione fisica |
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1 online resource (28 pages) |
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Collana |
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Report / 115th Congress, 2d session, House of Representatives ; ; 115-522 |
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Soggetti |
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Mortgage loans - Law and legislation - United States |
Settlement costs - United States |
Legislative materials. |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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2. |
Record Nr. |
UNINA9910788905703321 |
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Autore |
Karsh Efraim |
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Titolo |
The Soviet Union and Syria : the Asad years / / Efraim Karsh |
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Pubbl/distr/stampa |
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London ; ; New York : , : Routledge, , 2014 |
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ISBN |
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1-317-81850-4 |
1-315-81898-1 |
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Descrizione fisica |
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1 online resource (215 p.) |
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Collana |
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Routledge library editions. Syria ; ; volume 2 |
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Disciplina |
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Soggetti |
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Soviet Union Foreign relations Syria |
Syria Foreign relations Soviet Union |
Soviet Union Foreign relations 1953-1975 |
Soviet Union Foreign relations 1975-1985 |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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"First published in 1988"--T.p. verso. |
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Nota di bibliografia |
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Includes bibliographical references. |
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Nota di contenuto |
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Cover; Half Title; Title Page; Copyright Page; Original Title Page; Original Copyright Page; Table of Contents; Dedication; Acknowledgments; 1. Introduction; 2. Cooperation and conflict; The formative years; The October 1973 War; Disengagement; Closing the ranks; 3. Crisis over Lebanon; 4. Towards a bilateral treaty; Reconciliation; November 1977 and after; Towards a Friendship and Cooperation Treaty; 5. From crisis to war; The Syrian-Jordanian crisis; The Soviets and the missile crisis; Prelude to war; War over Lebanon; 6. From Brezhnev to Gorbachev; Chernenko and the Syrians |
Gorbachev: continuity or change?7. Conclusions; A marriage of convenience; Implications for the West; Notes; Appendices |
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Sommario/riassunto |
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This Chatham House Paper examines the nature of Soviet relations with Syria, assessing the commitments made and the gains reaped by Moscow and Damascus in the economic, military and political spheres. After discussing Soviet interests in the region in general and with regard to Syria in particular, the author traces the evolution of the relationship between Moscow and its major Middle Eastern ally since Asad came to power in 1970.While the study argues that huge Soviet military aid has intensified the pro-Soviet alignment of Syrian policy, it contends that Asad's perception of his co |
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3. |
Record Nr. |
UNIORUON00043260 |
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Autore |
TER-MKRTICJAN, L. X. |
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Titolo |
Armjanskie istocniki o Srednej Azii V-VII vv / L.X. Ter-Mkrticjan |
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Pubbl/distr/stampa |
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Moskva, : Izd. Nauka, Glavnaja Redakcija Vostocnoj Literatury, 1979 |
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Descrizione fisica |
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98 p., c. di tav. rip. ; 22 c |
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Classificazione |
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Soggetti |
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STORIOGRAFIA ARMENA - SEC. V-VII |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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In testa al front. : Akademija Nauk SSSR, Institut Vostokovedenija |
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4. |
Record Nr. |
UNINA9910768481303321 |
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Autore |
Brito Paula |
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Titolo |
Classification and Data Science in the Digital Age / / edited by Paula Brito, José G. Dias, Berthold Lausen, Angela Montanari, Rebecca Nugent |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023 |
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ISBN |
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Edizione |
[1st ed. 2023.] |
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Descrizione fisica |
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1 online resource (393 pages) |
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Collana |
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Studies in Classification, Data Analysis, and Knowledge Organization, , 2198-3321 |
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Altri autori (Persone) |
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DiasJosé G |
LausenBerthold |
MontanariAngela |
NugentRebecca |
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Disciplina |
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Soggetti |
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Artificial intelligence - Data processing |
Machine learning |
Data mining |
Multivariate analysis |
Statistics - Computer programs |
Data Science |
Statistical Learning |
Machine Learning |
Data Mining and Knowledge Discovery |
Multivariate Analysis |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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Preface -- R. Abdesselam: A Topological Clustering of Individuals -- C. Anton and I. Smith: Model Based Clustering of Functional Data with Mild Outliers -- F. Antonazzo and S. Ingrassia: A Trivariate Geometric Classification of Decision Boundaries for Mixtures of Regressions -- E. Arnone, E. Cunial, and L. M. Sangalli: Generalized Spatio-temporal Regression with PDE Penalization -- R. Ascari and S. Migliorati: A New Regression Model for the Analysis of Microbiome Data -- R. Aschenbruck, G. Szepannek, and A. F. X. Wilhelm: Stability of Mixed-type Cluster Partitions for Determination of the Number of Clusters -- A. Ashofteh and P. Campos: A Review on Official Survey Item Classification for Mixed-Mode Effects Adjustment -- V. Batagelj: Clustering and Blockmodeling Temporal Networks – Two Indirect Approaches -- R. Boutalbi, L. Labiod, and M. Nadif: Latent Block Regression Model -- N. Chabane, M. Achraf Bouaoune, R. Amir Sofiane Tighilt, B. Mazoure, N. Tahiri, and V. Makarenkov: Using Clustering and Machine Learning Methods to Provide Intelligent Grocery Shopping Recommendations -- T. Chadjipadelis and S. Magopoulou: COVID-19 Pandemic: a Methodological Model for the Analysis of Government’s Preventing Measures and Health Data Records -- J. Champagne Gareau, É. Beaudry, and V. Makarenkov: pcTVI: Parallel MDP Solver Using a Decomposition into Independent Chains -- C. Di Nuzzo and S. Ingrassia: Three-way Spectral Clustering -- J. Dobša and H. A. L. Kiers: Improving Classification of Documents by Semi-supervised Clustering in a Semantic Space -- J. Gama: Trends in Data Stream Mining -- L. A. García-Escudero, A. Mayo-Iscar, G. Morelli, and M. Riani: Old and New Constraints in Model Based Clustering -- V. G Genova, G. Giordano, G . Ragozini, and M. Prosperina Vitale: Clustering Student Mobility Data in 3-way Networks -- R. Giubilei: Clustering Brain Connectomes Through a Density-peak Approach -- T. Górecki, M. Šuczak, and P. Piasecki: Similarity Forest for Time Series Classification -- K. Hayashi, E. Hoshino, M. Suzuki, E. Nakanishi, K. Sakai, and M. Obatake: Detection of the Biliary Atresia Using Deep Convolutional Neural Networks Based on Statistical Learning Weights via Optimal Similarity and Resampling Methods -- Ch. Hennig: Some Issues in Robust Clustering -- J. Kalina and P. Janá£ek: Robustness Aspects of Optimized Centroids -- L. Labiod and M. Nadif: Data Clustering and Representation Learning Based on Networked Data -- Lazhar Labiod and Mohamed Nadif: Towards a Bi-stochastic Matrix Approximation of k-means and Some Variants -- A. LaLonde, T. Love, D. R. Young, and T. Wu: Clustering Adolescent Female Physical Activity Levels with an Infinite Mixture Model on Random Effects -- Á. López-Oriona, J. A. Vilar, and P. D’Urso: Unsupervised Classification of Categorical Time Series Through Innovative Distances -- D. Masís, E. Segura, J. Trejos, and A. Xavier: Fuzzy Clustering by Hyperbolic Smoothing -- R. Meng, H. K. H. Lee, and K. Bouchard: Stochastic Collapsed Variational Inference for Structured Gaussian Process Regression Networks -- H. Duy Nguyen, F. Forbes, G. Fort, and O. Cappé: An Online Minorization-Maximization |
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Algorithm -- L. Palazzo and R. Ievoli: Detecting Differences in Italian Regional Health Services During Two Covid-19 Waves -- G. Panagiotidou and T. Chadjipadelis: Political and Religion Attitudes in Greece: Behavioral Discourses -- K. Pawlasová, I. Karafiátová, and J. Dvořák: Supervised Classification via Neural Networks for Replicated Point Patterns -- G. Perrone and G. Soffritti: Parsimonious Mixtures of Seemingly Unrelated Contaminated Normal Regression Models -- N. Pronello, R. Ignaccolo, L. Ippoliti, and S. Fontanella: Penalized Model-based Functional Clustering: a Regularization Approach via Shrinkage Methods -- D. Rodrigues, L. P. Reis, and B. M. Faria: Emotion Classification Based on Single Electrode Brain Data: Applications for Assistive Technology -- R. Scimone, A. Menafoglio, L. M. Sangalli, and P. Secchi: The Death Process in Italy Before and During the Covid-19 Pandemic: a Functional Compositional Approach -- O. Silva, Á. Sousa, and H. Bacelar-Nicolau: Clustering Validation in the Context of Hierarchical Cluster Analysis: an Empirical Study -- C. Silvestre, M. G. M. S. Cardoso, and M. Figueiredo: An MML Embedded Approach for Estimating the Number of Clusters -- Á. Sousa, O. Silva, M. Graça Batista, S. Cabral, and H. Bacelar-Nicolau: Typology of Motivation Factors for Employees in the Banking Sector: An Empirical Study Using Multivariate Data Analysis Methods -- J. Michael Spoor, J. Weber, and J. Ovtcharova: A Proposal for Formalization and Definition of Anomalies in Dynamical Systems -- N. Tahiri and A. Koshkarov: New Metrics for Classifying Phylogenetic Trees Using -means and the Symmetric Difference Metric -- S. D. Tomarchio: On Parsimonious Modelling via Matrix-variate t Mixtures -- G. Zammarchi, M. Romano, and C. Conversano: Evolution of Media Coverage on Climate Change and Environmental Awareness: an Analysisof Tweets from UK and US Newspapers. |
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Sommario/riassunto |
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The contributions gathered in this open access book focus on modern methods for data science and classification and present a series of real-world applications. Numerous research topics are covered, ranging from statistical inference and modeling to clustering and dimension reduction, from functional data analysis to time series analysis, and network analysis. The applications reflect new analyses in a variety of fields, including medicine, marketing, genetics, engineering, and education. The book comprises selected and peer-reviewed papers presented at the 17th Conference of the International Federation of Classification Societies (IFCS 2022), held in Porto, Portugal, July 19–23, 2022. The IFCS federates the classification societies and the IFCS biennial conference brings together researchers and stakeholders in the areas of Data Science, Classification, and Machine Learning. It provides a forum for presenting high-quality theoretical and applied works, and promoting and fostering interdisciplinary research and international cooperation. The intended audience is researchers and practitioners who seek the latest developments and applications in the field of data science and classification. |
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